From 00d4fb060842ec4efc98fa714219cd0274ec93f7 Mon Sep 17 00:00:00 2001
From: Mingjer Lee <mingjerli@gmail.com>
Date: Thu, 10 Aug 2023 23:15:30 +0800
Subject: [PATCH] LIN-748-tutorial for reusable compoennt (#881)

* Add Reusable Component tutorial

* Refresh demos folder and update docs

* update kernal name

* Refresh demos folder and update docs

* Minor typo fix

* Refresh demos folder and update docs

* Fix dependencies

* Refresh demos folder and update docs

* skip tensorflow cells

---------

Co-authored-by: Humble bot servant <infra@linea.ai>
---
 .colab/03_reusable_components.ipynb           | 1845 +++++++++++++++++
 .../tutorials/03_reusable_components.ipynb    | 1845 +++++++++++++++++
 2 files changed, 3690 insertions(+)
 create mode 100644 .colab/03_reusable_components.ipynb
 create mode 100644 examples/tutorials/03_reusable_components.ipynb

diff --git a/.colab/03_reusable_components.ipynb b/.colab/03_reusable_components.ipynb
new file mode 100644
index 000000000..6b6ec5c27
--- /dev/null
+++ b/.colab/03_reusable_components.ipynb
@@ -0,0 +1,1845 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "fa3d2a9c-442b-44c2-bf4d-45bb544ec369",
+    "_uuid": "bcba1675eebc008a35c21f6b64174161ff7d4c48",
+    "id": "4a1bScthQFMl"
+   },
+   "source": [
+    "# Reusable Components with LineaPy\n",
+    "\n",
+    "This tutorial will use a typical data science workflow, including the following steps.\n",
+    "\n",
+    "1. reading raw data\n",
+    "1. doing exploratory data analysis\n",
+    "1. doing feature engineering\n",
+    "1. training an ML model\n",
+    "1. evaluating model performance\n",
+    "\n",
+    "As our example to demonstrate how to use `LineaPy` to create reusable components and how we can reuse these components."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "i-i9q7mtPGHZ",
+    "tags": []
+   },
+   "source": [
+    "## Part 1: Go Through a Typical Data Scientist Workflow\n",
+    "\n",
+    "Let's use [Sloan Digital Sky Survey Classification](https://www.kaggle.com/datasets/lucidlenn/sloan-digital-sky-survey) as our example. \n",
+    "The data consists of 10,000 observations of space taken by the [SDSS](http://www.sdss.org/dr14/). \n",
+    "Every observation is described by 17 feature columns and 1 class column, identifying it as a star, galaxy, or quasar.\n",
+    "Our example is training a model to predict the object class(galaxy, star, or quasar) based on the 17 features.\n",
+    "\n",
+    "Detailed dataset descriptions are available at [Sloan Digital Sky Survey Classification](https://www.kaggle.com/datasets/lucidlenn/sloan-digital-sky-survey).\n",
+    "More background education about galaxies is available at [UMD ASTR620 class](https://www.astro.umd.edu/~richard/ASTRO620/index_fall2015.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "b9d4b609-9f0f-4296-8462-98975b2ece09",
+    "_uuid": "16d228ddb3b0d71d6e13093552c04a21146b75e5",
+    "id": "IeFWMBAZQFMo",
+    "tags": []
+   },
+   "source": [
+    "### Importing Libraries and Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "!pip -q install lineapy~=0.2 scikit-learn pandas matplotlib seaborn numpy tensorflow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "w0H6N-TzQQ94",
+    "outputId": "3cdb6e4d-fbfc-4143-8378-6f91a0080237"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mingjerli/miniconda3/envs/lineapy39/lib/python3.9/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "%load_ext lineapy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "_cell_guid": "13914766-c2fb-4801-8846-6c78e6d1cb03",
+    "_uuid": "5bb212bdb34abc34f8bed1a0bc2d1a6287166221",
+    "id": "Yz8i8KUNQFMp"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib import style\n",
+    "import seaborn as sns\n",
+    "sns.set_style('whitegrid')\n",
+    "from sklearn.model_selection import train_test_split, cross_val_predict\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.linear_model import SGDClassifier\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "from sklearn.decomposition import PCA\n",
+    "from sklearn.preprocessing import LabelEncoder, MinMaxScaler\n",
+    "from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score\n",
+    "import time\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n",
+    "\n",
+    "import importlib\n",
+    "import sklearn\n",
+    "import importlib.util\n",
+    "import sys\n",
+    "import tempfile\n",
+    "from importlib.abc import Loader\n",
+    "from pathlib import Path\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "SMALL_SIZE = 10\n",
+    "MEDIUM_SIZE = 12\n",
+    "\n",
+    "plt.rcParams['figure.dpi']=240"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "id": "sP_le67aWA8n"
+   },
+   "outputs": [],
+   "source": [
+    "def flowchart(graph, dpi = 240, title=\"\"):\n",
+    "    \"\"\"\n",
+    "    Draw mermaid diagram in notebook\n",
+    "    \n",
+    "    We use this to visualize some diagrams in the rest of the  notebook, it's not part of LineaPy\n",
+    "    \"\"\"\n",
+    "    import base64\n",
+    "    import requests, io\n",
+    "    from PIL import Image\n",
+    "    import matplotlib.pyplot as plt\n",
+    "\n",
+    "    graphbytes = graph.encode(\"ascii\")\n",
+    "    base64_bytes = base64.b64encode(graphbytes)\n",
+    "    base64_string = base64_bytes.decode(\"ascii\")\n",
+    "    img = Image.open(io.BytesIO(requests.get('https://mermaid.ink/img/' + base64_string).content))\n",
+    "    plt.rcParams['figure.dpi']=dpi\n",
+    "    plt.imshow(img)\n",
+    "    plt.grid(None)\n",
+    "    plt.xticks([])\n",
+    "    plt.yticks([])\n",
+    "    plt.title(title)          "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "AGgT4L23Nilj",
+    "tags": []
+   },
+   "source": [
+    "### Load Dataset\n",
+    "\n",
+    "Load Data and Save Artifact as CheckPoint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "_cell_guid": "ffb06ef6-73f7-4f42-ab42-5d5b5f773ba7",
+    "_uuid": "04e88f8c9c12167a1c23e47b3e2046246510e983",
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "-xtqrprxQFMr",
+    "outputId": "afd4955d-d2f1-4676-c79b-147eae0ee19b"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LineaArtifact(name='raw_data', _version=10)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "url = \"https://raw.githubusercontent.com/LineaLabs/lineapy/main/examples/use_cases/creating_reusable_components/data/Skyserver_SQL2_27_2018%206_51_39%20PM.csv\"\n",
+    "sdss_df = pd.read_csv(url)\n",
+    "lineapy.save(sdss_df, 'raw_data')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "75a847f1-0dfc-4228-9cbc-49d6034463e5",
+    "_uuid": "9b01bc847e158cfa00d411ea687cb573a0037aef",
+    "id": "hyR_FbSwQFMs",
+    "tags": []
+   },
+   "source": [
+    "### Explorer the Dataset\n",
+    "\n",
+    "The dataset has 10000 examples, 17 feature columns, and one target column. 8 of the 17 features are 64-bit integers, one feature is an unsigned 64-bit integer, 8 are 64-bit floats, and the target column is of the type object. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 270
+    },
+    "id": "4Je22tYwPqF-",
+    "outputId": "67929a67-7ebb-4e8f-8d26-b1989ad66c70"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>objid</th>\n",
+       "      <th>ra</th>\n",
+       "      <th>dec</th>\n",
+       "      <th>u</th>\n",
+       "      <th>g</th>\n",
+       "      <th>r</th>\n",
+       "      <th>i</th>\n",
+       "      <th>z</th>\n",
+       "      <th>run</th>\n",
+       "      <th>rerun</th>\n",
+       "      <th>camcol</th>\n",
+       "      <th>field</th>\n",
+       "      <th>specobjid</th>\n",
+       "      <th>class</th>\n",
+       "      <th>redshift</th>\n",
+       "      <th>plate</th>\n",
+       "      <th>mjd</th>\n",
+       "      <th>fiberid</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.237650e+18</td>\n",
+       "      <td>183.531326</td>\n",
+       "      <td>0.089693</td>\n",
+       "      <td>19.47406</td>\n",
+       "      <td>17.04240</td>\n",
+       "      <td>15.94699</td>\n",
+       "      <td>15.50342</td>\n",
+       "      <td>15.22531</td>\n",
+       "      <td>752</td>\n",
+       "      <td>301</td>\n",
+       "      <td>4</td>\n",
+       "      <td>267</td>\n",
+       "      <td>3.722360e+18</td>\n",
+       "      <td>STAR</td>\n",
+       "      <td>-0.000009</td>\n",
+       "      <td>3306</td>\n",
+       "      <td>54922</td>\n",
+       "      <td>491</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.237650e+18</td>\n",
+       "      <td>183.598370</td>\n",
+       "      <td>0.135285</td>\n",
+       "      <td>18.66280</td>\n",
+       "      <td>17.21449</td>\n",
+       "      <td>16.67637</td>\n",
+       "      <td>16.48922</td>\n",
+       "      <td>16.39150</td>\n",
+       "      <td>752</td>\n",
+       "      <td>301</td>\n",
+       "      <td>4</td>\n",
+       "      <td>267</td>\n",
+       "      <td>3.638140e+17</td>\n",
+       "      <td>STAR</td>\n",
+       "      <td>-0.000055</td>\n",
+       "      <td>323</td>\n",
+       "      <td>51615</td>\n",
+       "      <td>541</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.237650e+18</td>\n",
+       "      <td>183.680207</td>\n",
+       "      <td>0.126185</td>\n",
+       "      <td>19.38298</td>\n",
+       "      <td>18.19169</td>\n",
+       "      <td>17.47428</td>\n",
+       "      <td>17.08732</td>\n",
+       "      <td>16.80125</td>\n",
+       "      <td>752</td>\n",
+       "      <td>301</td>\n",
+       "      <td>4</td>\n",
+       "      <td>268</td>\n",
+       "      <td>3.232740e+17</td>\n",
+       "      <td>GALAXY</td>\n",
+       "      <td>0.123111</td>\n",
+       "      <td>287</td>\n",
+       "      <td>52023</td>\n",
+       "      <td>513</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.237650e+18</td>\n",
+       "      <td>183.870529</td>\n",
+       "      <td>0.049911</td>\n",
+       "      <td>17.76536</td>\n",
+       "      <td>16.60272</td>\n",
+       "      <td>16.16116</td>\n",
+       "      <td>15.98233</td>\n",
+       "      <td>15.90438</td>\n",
+       "      <td>752</td>\n",
+       "      <td>301</td>\n",
+       "      <td>4</td>\n",
+       "      <td>269</td>\n",
+       "      <td>3.722370e+18</td>\n",
+       "      <td>STAR</td>\n",
+       "      <td>-0.000111</td>\n",
+       "      <td>3306</td>\n",
+       "      <td>54922</td>\n",
+       "      <td>510</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.237650e+18</td>\n",
+       "      <td>183.883288</td>\n",
+       "      <td>0.102557</td>\n",
+       "      <td>17.55025</td>\n",
+       "      <td>16.26342</td>\n",
+       "      <td>16.43869</td>\n",
+       "      <td>16.55492</td>\n",
+       "      <td>16.61326</td>\n",
+       "      <td>752</td>\n",
+       "      <td>301</td>\n",
+       "      <td>4</td>\n",
+       "      <td>269</td>\n",
+       "      <td>3.722370e+18</td>\n",
+       "      <td>STAR</td>\n",
+       "      <td>0.000590</td>\n",
+       "      <td>3306</td>\n",
+       "      <td>54922</td>\n",
+       "      <td>512</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          objid          ra       dec         u         g         r         i  \\\n",
+       "0  1.237650e+18  183.531326  0.089693  19.47406  17.04240  15.94699  15.50342   \n",
+       "1  1.237650e+18  183.598370  0.135285  18.66280  17.21449  16.67637  16.48922   \n",
+       "2  1.237650e+18  183.680207  0.126185  19.38298  18.19169  17.47428  17.08732   \n",
+       "3  1.237650e+18  183.870529  0.049911  17.76536  16.60272  16.16116  15.98233   \n",
+       "4  1.237650e+18  183.883288  0.102557  17.55025  16.26342  16.43869  16.55492   \n",
+       "\n",
+       "          z  run  rerun  camcol  field     specobjid   class  redshift  plate  \\\n",
+       "0  15.22531  752    301       4    267  3.722360e+18    STAR -0.000009   3306   \n",
+       "1  16.39150  752    301       4    267  3.638140e+17    STAR -0.000055    323   \n",
+       "2  16.80125  752    301       4    268  3.232740e+17  GALAXY  0.123111    287   \n",
+       "3  15.90438  752    301       4    269  3.722370e+18    STAR -0.000111   3306   \n",
+       "4  16.61326  752    301       4    269  3.722370e+18    STAR  0.000590   3306   \n",
+       "\n",
+       "     mjd  fiberid  \n",
+       "0  54922      491  \n",
+       "1  51615      541  \n",
+       "2  52023      513  \n",
+       "3  54922      510  \n",
+       "4  54922      512  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "sdss_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "_cell_guid": "6723c745-8446-46f0-a866-8c22668607d3",
+    "_uuid": "77495f8526975b41e2ba43063b82d807e8ba1109",
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 364
+    },
+    "id": "zLigzi31QFMs",
+    "outputId": "d9dd7b32-7e54-42cb-a300-f6b86ecd7028",
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>objid</th>\n",
+       "      <th>ra</th>\n",
+       "      <th>dec</th>\n",
+       "      <th>u</th>\n",
+       "      <th>g</th>\n",
+       "      <th>r</th>\n",
+       "      <th>i</th>\n",
+       "      <th>z</th>\n",
+       "      <th>run</th>\n",
+       "      <th>rerun</th>\n",
+       "      <th>camcol</th>\n",
+       "      <th>field</th>\n",
+       "      <th>specobjid</th>\n",
+       "      <th>redshift</th>\n",
+       "      <th>plate</th>\n",
+       "      <th>mjd</th>\n",
+       "      <th>fiberid</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>1.000000e+04</td>\n",
+       "      <td>10000.000000</td>\n",
+       "      <td>10000.000000</td>\n",
+       "      <td>10000.000000</td>\n",
+       "      <td>10000.000000</td>\n",
+       "      <td>10000.000000</td>\n",
+       "      <td>10000.000000</td>\n",
+       "      <td>10000.000000</td>\n",
+       "      <td>10000.000000</td>\n",
+       "      <td>10000.0</td>\n",
+       "      <td>10000.000000</td>\n",
+       "      <td>10000.000000</td>\n",
+       "      <td>1.000000e+04</td>\n",
+       "      <td>10000.000000</td>\n",
+       "      <td>10000.000000</td>\n",
+       "      <td>10000.000000</td>\n",
+       "      <td>10000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>1.237650e+18</td>\n",
+       "      <td>175.529987</td>\n",
+       "      <td>14.836148</td>\n",
+       "      <td>18.619355</td>\n",
+       "      <td>17.371931</td>\n",
+       "      <td>16.840963</td>\n",
+       "      <td>16.583579</td>\n",
+       "      <td>16.422833</td>\n",
+       "      <td>981.034800</td>\n",
+       "      <td>301.0</td>\n",
+       "      <td>3.648700</td>\n",
+       "      <td>302.380100</td>\n",
+       "      <td>1.645022e+18</td>\n",
+       "      <td>0.143726</td>\n",
+       "      <td>1460.986400</td>\n",
+       "      <td>52943.533300</td>\n",
+       "      <td>353.069400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>47.783439</td>\n",
+       "      <td>25.212207</td>\n",
+       "      <td>0.828656</td>\n",
+       "      <td>0.945457</td>\n",
+       "      <td>1.067764</td>\n",
+       "      <td>1.141805</td>\n",
+       "      <td>1.203188</td>\n",
+       "      <td>273.305024</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.666183</td>\n",
+       "      <td>162.577763</td>\n",
+       "      <td>2.013998e+18</td>\n",
+       "      <td>0.388774</td>\n",
+       "      <td>1788.778371</td>\n",
+       "      <td>1511.150651</td>\n",
+       "      <td>206.298149</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.237650e+18</td>\n",
+       "      <td>8.235100</td>\n",
+       "      <td>-5.382632</td>\n",
+       "      <td>12.988970</td>\n",
+       "      <td>12.799550</td>\n",
+       "      <td>12.431600</td>\n",
+       "      <td>11.947210</td>\n",
+       "      <td>11.610410</td>\n",
+       "      <td>308.000000</td>\n",
+       "      <td>301.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>2.995780e+17</td>\n",
+       "      <td>-0.004136</td>\n",
+       "      <td>266.000000</td>\n",
+       "      <td>51578.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>1.237650e+18</td>\n",
+       "      <td>157.370946</td>\n",
+       "      <td>-0.539035</td>\n",
+       "      <td>18.178035</td>\n",
+       "      <td>16.815100</td>\n",
+       "      <td>16.173333</td>\n",
+       "      <td>15.853705</td>\n",
+       "      <td>15.618285</td>\n",
+       "      <td>752.000000</td>\n",
+       "      <td>301.0</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>184.000000</td>\n",
+       "      <td>3.389248e+17</td>\n",
+       "      <td>0.000081</td>\n",
+       "      <td>301.000000</td>\n",
+       "      <td>51900.000000</td>\n",
+       "      <td>186.750000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>1.237650e+18</td>\n",
+       "      <td>180.394514</td>\n",
+       "      <td>0.404166</td>\n",
+       "      <td>18.853095</td>\n",
+       "      <td>17.495135</td>\n",
+       "      <td>16.858770</td>\n",
+       "      <td>16.554985</td>\n",
+       "      <td>16.389945</td>\n",
+       "      <td>756.000000</td>\n",
+       "      <td>301.0</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>299.000000</td>\n",
+       "      <td>4.966580e+17</td>\n",
+       "      <td>0.042591</td>\n",
+       "      <td>441.000000</td>\n",
+       "      <td>51997.000000</td>\n",
+       "      <td>351.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>1.237650e+18</td>\n",
+       "      <td>201.547279</td>\n",
+       "      <td>35.649397</td>\n",
+       "      <td>19.259232</td>\n",
+       "      <td>18.010145</td>\n",
+       "      <td>17.512675</td>\n",
+       "      <td>17.258550</td>\n",
+       "      <td>17.141447</td>\n",
+       "      <td>1331.000000</td>\n",
+       "      <td>301.0</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>414.000000</td>\n",
+       "      <td>2.881300e+18</td>\n",
+       "      <td>0.092579</td>\n",
+       "      <td>2559.000000</td>\n",
+       "      <td>54468.000000</td>\n",
+       "      <td>510.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>1.237650e+18</td>\n",
+       "      <td>260.884382</td>\n",
+       "      <td>68.542265</td>\n",
+       "      <td>19.599900</td>\n",
+       "      <td>19.918970</td>\n",
+       "      <td>24.802040</td>\n",
+       "      <td>28.179630</td>\n",
+       "      <td>22.833060</td>\n",
+       "      <td>1412.000000</td>\n",
+       "      <td>301.0</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>768.000000</td>\n",
+       "      <td>9.468830e+18</td>\n",
+       "      <td>5.353854</td>\n",
+       "      <td>8410.000000</td>\n",
+       "      <td>57481.000000</td>\n",
+       "      <td>1000.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              objid            ra           dec             u             g  \\\n",
+       "count  1.000000e+04  10000.000000  10000.000000  10000.000000  10000.000000   \n",
+       "mean   1.237650e+18    175.529987     14.836148     18.619355     17.371931   \n",
+       "std    0.000000e+00     47.783439     25.212207      0.828656      0.945457   \n",
+       "min    1.237650e+18      8.235100     -5.382632     12.988970     12.799550   \n",
+       "25%    1.237650e+18    157.370946     -0.539035     18.178035     16.815100   \n",
+       "50%    1.237650e+18    180.394514      0.404166     18.853095     17.495135   \n",
+       "75%    1.237650e+18    201.547279     35.649397     19.259232     18.010145   \n",
+       "max    1.237650e+18    260.884382     68.542265     19.599900     19.918970   \n",
+       "\n",
+       "                  r             i             z           run    rerun  \\\n",
+       "count  10000.000000  10000.000000  10000.000000  10000.000000  10000.0   \n",
+       "mean      16.840963     16.583579     16.422833    981.034800    301.0   \n",
+       "std        1.067764      1.141805      1.203188    273.305024      0.0   \n",
+       "min       12.431600     11.947210     11.610410    308.000000    301.0   \n",
+       "25%       16.173333     15.853705     15.618285    752.000000    301.0   \n",
+       "50%       16.858770     16.554985     16.389945    756.000000    301.0   \n",
+       "75%       17.512675     17.258550     17.141447   1331.000000    301.0   \n",
+       "max       24.802040     28.179630     22.833060   1412.000000    301.0   \n",
+       "\n",
+       "             camcol         field     specobjid      redshift         plate  \\\n",
+       "count  10000.000000  10000.000000  1.000000e+04  10000.000000  10000.000000   \n",
+       "mean       3.648700    302.380100  1.645022e+18      0.143726   1460.986400   \n",
+       "std        1.666183    162.577763  2.013998e+18      0.388774   1788.778371   \n",
+       "min        1.000000     11.000000  2.995780e+17     -0.004136    266.000000   \n",
+       "25%        2.000000    184.000000  3.389248e+17      0.000081    301.000000   \n",
+       "50%        4.000000    299.000000  4.966580e+17      0.042591    441.000000   \n",
+       "75%        5.000000    414.000000  2.881300e+18      0.092579   2559.000000   \n",
+       "max        6.000000    768.000000  9.468830e+18      5.353854   8410.000000   \n",
+       "\n",
+       "                mjd       fiberid  \n",
+       "count  10000.000000  10000.000000  \n",
+       "mean   52943.533300    353.069400  \n",
+       "std     1511.150651    206.298149  \n",
+       "min    51578.000000      1.000000  \n",
+       "25%    51900.000000    186.750000  \n",
+       "50%    51997.000000    351.000000  \n",
+       "75%    54468.000000    510.000000  \n",
+       "max    57481.000000   1000.000000  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "sdss_df.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "10fc6f0d-fad0-44ca-8996-bdf513e48358",
+    "_uuid": "2fe1eb4a169a3400d65c3f5d3134ae8fb250240e",
+    "id": "5Fyi0tHiQFMu",
+    "tags": []
+   },
+   "source": [
+    "### Feature Engineering\n",
+    "\n",
+    "#### u, g, r, i, z\n",
+    "\n",
+    "We will now reduce the number of dimensions by replacing the different bands 'u,' 'g,' 'r,' 'i', and 'z' with a linear combination with only three dimensions using **Principal Component Analysis**.\n",
+    "\n",
+    "**Principal Component Analysis:**\n",
+    "\n",
+    "n observations with p features can be interpreted as n points in p-dimensional space. PCA aims to project this space into a q-dimensional subspace (with q<p) with as little information loss as possible. \n",
+    "\n",
+    "It finds the q directions in which the n points vary the most (--> the principal components). It then projects the original data points into the q-dimensional subspace. PCA returns a n x q dimensional matrix. \n",
+    "\n",
+    "Using PCA on our data will decrease the number of operations during training and testing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "_cell_guid": "fbb45762-272f-40b8-bde6-9d3dd9c1cd55",
+    "_uuid": "8a97dca248a7b0473c784af669ea00b59017fa8a",
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 206
+    },
+    "id": "hjP6UjNjQFMu",
+    "outputId": "390e7b44-99a4-452d-f111-909f13724971"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ra</th>\n",
+       "      <th>dec</th>\n",
+       "      <th>class</th>\n",
+       "      <th>redshift</th>\n",
+       "      <th>plate</th>\n",
+       "      <th>mjd</th>\n",
+       "      <th>fiberid</th>\n",
+       "      <th>PCA_1</th>\n",
+       "      <th>PCA_2</th>\n",
+       "      <th>PCA_3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>183.531326</td>\n",
+       "      <td>0.089693</td>\n",
+       "      <td>2</td>\n",
+       "      <td>-0.000009</td>\n",
+       "      <td>3306</td>\n",
+       "      <td>54922</td>\n",
+       "      <td>491</td>\n",
+       "      <td>-1.507202</td>\n",
+       "      <td>-1.377293</td>\n",
+       "      <td>-0.265119</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>183.598370</td>\n",
+       "      <td>0.135285</td>\n",
+       "      <td>2</td>\n",
+       "      <td>-0.000055</td>\n",
+       "      <td>323</td>\n",
+       "      <td>51615</td>\n",
+       "      <td>541</td>\n",
+       "      <td>-0.195758</td>\n",
+       "      <td>-0.028410</td>\n",
+       "      <td>-0.155695</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>183.680207</td>\n",
+       "      <td>0.126185</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.123111</td>\n",
+       "      <td>287</td>\n",
+       "      <td>52023</td>\n",
+       "      <td>513</td>\n",
+       "      <td>1.297604</td>\n",
+       "      <td>-0.590023</td>\n",
+       "      <td>0.140338</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>183.870529</td>\n",
+       "      <td>0.049911</td>\n",
+       "      <td>2</td>\n",
+       "      <td>-0.000111</td>\n",
+       "      <td>3306</td>\n",
+       "      <td>54922</td>\n",
+       "      <td>510</td>\n",
+       "      <td>-1.446117</td>\n",
+       "      <td>0.566685</td>\n",
+       "      <td>-0.009272</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>183.883288</td>\n",
+       "      <td>0.102557</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.000590</td>\n",
+       "      <td>3306</td>\n",
+       "      <td>54922</td>\n",
+       "      <td>512</td>\n",
+       "      <td>-0.849271</td>\n",
+       "      <td>1.287505</td>\n",
+       "      <td>-0.397689</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           ra       dec  class  redshift  plate    mjd  fiberid     PCA_1  \\\n",
+       "0  183.531326  0.089693      2 -0.000009   3306  54922      491 -1.507202   \n",
+       "1  183.598370  0.135285      2 -0.000055    323  51615      541 -0.195758   \n",
+       "2  183.680207  0.126185      0  0.123111    287  52023      513  1.297604   \n",
+       "3  183.870529  0.049911      2 -0.000111   3306  54922      510 -1.446117   \n",
+       "4  183.883288  0.102557      2  0.000590   3306  54922      512 -0.849271   \n",
+       "\n",
+       "      PCA_2     PCA_3  \n",
+       "0 -1.377293 -0.265119  \n",
+       "1 -0.028410 -0.155695  \n",
+       "2 -0.590023  0.140338  \n",
+       "3  0.566685 -0.009272  \n",
+       "4  1.287505 -0.397689  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "# Keep only feature and target columns\n",
+    "sdss_df_fe = sdss_df.drop(['objid', 'run', 'rerun', 'camcol', 'field', 'specobjid'], axis=1)\n",
+    "\n",
+    "# encode class labels to integers\n",
+    "le = LabelEncoder()\n",
+    "y_encoded = le.fit_transform(sdss_df_fe['class'])\n",
+    "sdss_df_fe['class'] = y_encoded\n",
+    "\n",
+    "# Principal Component Analysis\n",
+    "pca = PCA(n_components=3)\n",
+    "ugriz = pca.fit_transform(sdss_df_fe[['u', 'g', 'r', 'i', 'z']])\n",
+    "\n",
+    "# update dataframe \n",
+    "sdss_df_fe = pd.concat((sdss_df_fe, pd.DataFrame(ugriz)), axis=1)\n",
+    "sdss_df_fe.rename({0: 'PCA_1', 1: 'PCA_2', 2: 'PCA_3'}, axis=1, inplace = True)\n",
+    "sdss_df_fe.drop(['u', 'g', 'r', 'i', 'z'], axis=1, inplace=True)\n",
+    "\n",
+    "# Save artifact and register checkpoint\n",
+    "lineapy.save(sdss_df_fe, 'fe_data')\n",
+    "sdss_df_fe.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "4e797850-cbb6-44f8-84d8-2649d9dcb461",
+    "_uuid": "73423e72a3e388e8d6b8f16672a7b77215359cfe",
+    "id": "SxwYeo_jQFMu",
+    "tags": []
+   },
+   "source": [
+    "### Machine Learning Models - Training"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "PS9EP1qyOJn6"
+   },
+   "source": [
+    "#### Feature Scaling\n",
+    "\n",
+    "We will now train different models on this dataset. \n",
+    "Scaling all values to be within the (0, 1) interval will reduce the distortion due to exceptionally high values and make some algorithms converge faster."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "_cell_guid": "23fc8398-331d-4164-8779-0516264ece29",
+    "_uuid": "c147b9e899cc2dd508d736274c856f88fb49321d",
+    "id": "33WC-SSjQFMu"
+   },
+   "outputs": [],
+   "source": [
+    "scaler = MinMaxScaler()\n",
+    "sdss = scaler.fit_transform(sdss_df_fe.drop('class', axis=1))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "13e1fd6f-820c-4098-a486-0079b300e6c9",
+    "_uuid": "e76e964a4b93c27e1ab01c24a06be09d8b304970",
+    "id": "MZlx5sj6QFMu"
+   },
+   "source": [
+    "#### Train Test Split\n",
+    "We will split the data into a training and a test part and use the training set for training the model and the test set for validation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "id": "Yf_VKm0o_utP"
+   },
+   "outputs": [],
+   "source": [
+    " X_train, X_test, y_train, y_test= train_test_split(sdss, sdss_df_fe['class'], test_size=0.33)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Xz0znPYjQFMu"
+   },
+   "source": [
+    "#### Train Model\n",
+    "\n",
+    "Here, we are training an SVC model. Note that instead of using `from sklearn.svm import SVC` to initiate the model instance, we wrote a general wrapper to create any `sklearn` model by name for reusability purposes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "TVS7WN-ruqP2",
+    "outputId": "85d0bf02-2834-4bd4-945e-f472a67a231c"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LineaArtifact(name='model', _version=9)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "def get_sklearn_model(\n",
+    "    import_module: str, model_name: str, model_params: dict\n",
+    "):\n",
+    "    \"\"\"Returns a scikit-learn model\"\"\"\n",
+    "    model_class = getattr(importlib.import_module(import_module), model_name)\n",
+    "    model = model_class(**model_params)  # Instantiates the model\n",
+    "    return model\n",
+    "\n",
+    "import_module = \"sklearn.svm\"\n",
+    "model_name = \"SVC\"\n",
+    "model_configuration = {}\n",
+    "model = get_sklearn_model(import_module, model_name, model_configuration)\n",
+    "\n",
+    "# Train the model\n",
+    "model.fit(X_train, y_train)\n",
+    "\n",
+    "# Save model as artifact for checkpoint\n",
+    "lineapy.save(model, \"model\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "9jQsjqWCOjfN",
+    "tags": []
+   },
+   "source": [
+    "### Evaluate Model performance\n",
+    "\n",
+    "Here, we perform k-fold cross-validation to get a more realistic result by testing the performance for ten different train and test datasets and averaging the results. Cross-validation ensures that the above result is not arbitrary and gives a more reliable performance check."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "hVA4arodHglE",
+    "outputId": "4c1fc5f0-4186-4a99-f42a-7848d6a81e3c"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'accuracy': 93.6969696969697, 'cv_scores': array([0.95223881, 0.9238806 , 0.94477612, 0.94477612, 0.94626866,\n",
+      "       0.94626866, 0.95223881, 0.93432836, 0.94029851, 0.93432836]), 'cv_scoring_metric': 0.9419402985074627}\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# Evaluating the Model Performance\n",
+    "\n",
+    "preds = model.predict(X_test)\n",
+    "# model accuracies\n",
+    "acc = (preds == y_test).sum().astype(float) / len(preds)*100\n",
+    "\n",
+    "# Cross Validation\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "\n",
+    "scoring = \"accuracy\"\n",
+    "cv_scores = cross_val_score(model, X_train, y_train, cv=10, scoring = scoring)\n",
+    "cv_scoring_metric_name = 'mean'\n",
+    "cv_scoring_metric = getattr(cv_scores, cv_scoring_metric_name)()\n",
+    "\n",
+    "# Combine train/test split accuracy with CV accuracy\n",
+    "model_metrics = {\n",
+    "    scoring: acc,\n",
+    "    \"cv_scores\": cv_scores,\n",
+    "    \"cv_scoring_metric\": cv_scoring_metric\n",
+    "}\n",
+    "\n",
+    "# Save model performance metric as checkpoint\n",
+    "art = lineapy.save(model_metrics, \"model_metrics\")\n",
+    "\n",
+    "print(model_metrics)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "wDkEdDOJPMgH"
+   },
+   "source": [
+    "### Recap\n",
+    "\n",
+    "So far, we've developed the above code to\n",
+    "\n",
+    "* Read raw data from the data source (and save the raw data as an artifact)\n",
+    "* Done exploratory data analysis\n",
+    "* Performed feature engineering (and saved the engineered feature as an artifact)\n",
+    "* Trained an SVC model (and saved the trained model as an artifact)\n",
+    "* Evaluated the SVC model (and saved the metric as an artifact)\n",
+    "\n",
+    "We saved some artifacts as checkpoints(in the following ordering) to help us create reusable components from the above code we've executed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 190
+    },
+    "id": "T8NhG8SROzfd",
+    "jupyter": {
+     "source_hidden": true
+    },
+    "outputId": "ffd670a3-9c85-4d2b-bfd1-876920a4903c",
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "graph = \"\"\"\n",
+    "graph LR;\n",
+    "RD[raw_data] --> FD[fe_data]\n",
+    "FD --> M[model]\n",
+    "M --> MM[model_metrics]\n",
+    "\"\"\"\n",
+    "\n",
+    "flowchart(graph, 200, 'Artifact Sequence')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "O_eSP0Mornrk",
+    "tags": []
+   },
+   "source": [
+    "## Part 2: Create Reusable Components\n",
+    "\n",
+    "During the model development process, there are many reasons to modify the above code, such as\n",
+    "* Using different data and redoing the end-to-end process to adapt more realistic cases.\n",
+    "* Using different feature sets to improve the model.\n",
+    "* Training different models to find the best model.\n",
+    "* Using different metrics to align with business objectives.\n",
+    "\n",
+    "The conventional wisdom for the user to achieve these goals are\n",
+    "* At the early stage of development, data scientists will make several copies of the notebook and make some changes here and there; they eventually lose track of the notebook.\n",
+    "* At the later stage of the development, data engineers will refactor the code as a standalone module and share the library across the team. The module becomes very rigid and slow-moving if any changes need to happen so that data scientists will go back to the notebook copying path again.\n",
+    "\n",
+    "`LineaPy` provides two methods to reuse the workflow from their notebook. There is no need to duplicate the notebook endlessly but still keep the development process flexible enough. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "9aE9IYTSU7hN"
+   },
+   "source": [
+    "\n",
+    "### 1. Reusable component as a Python function. \n",
+    "\n",
+    "`LineaPy` can generate a parameterized workflow that calculates the list of variables(previously registered as artifacts) as a Python function.\n",
+    "This can be achieved by specifying a list of input variables users want to parameterize and the list of artifacts(checkpoints) they want to recompute to `lineapy.get_function(artifacts, input_parameters)` API.\n",
+    "\n",
+    "Let's say we want to train a stochastic gradient descent(SGD) model and use minimal accuracy as our metric to evaluate the model. \n",
+    "We can achieve this by following code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Hvde5HeVcchY",
+    "outputId": "14b6855d-7458-4b61-ee4e-79eb1e0aec52"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'model_metrics': {'accuracy': 90.87878787878788,\n",
+       "  'cv_scores': array([0.9238806 , 0.92238806, 0.91343284, 0.90447761, 0.91791045,\n",
+       "         0.92238806, 0.91791045, 0.89552239, 0.94029851, 0.9238806 ]),\n",
+       "  'cv_scoring_metric': 0.8955223880597015}}"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "# Want to use different model and evaluate with different scoring metric.\n",
+    "\n",
+    "calculate_metrics = lineapy.get_function(\n",
+    "  artifacts=[\"model_metrics\"], \n",
+    "  input_parameters=[\"import_module\",\"model_name\",\"scoring\",\"cv_scoring_metric_name\"], \n",
+    ")\n",
+    "\n",
+    "sgd_metrics = calculate_metrics(\n",
+    "    import_module='sklearn.linear_model', \n",
+    "    model_name='SGDClassifier', \n",
+    "    scoring='accuracy', \n",
+    "    cv_scoring_metric_name='min'\n",
+    ")\n",
+    "sgd_metrics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "tRhqG4apwHZ9"
+   },
+   "source": [
+    "### 2. Reusable components as a Python module. \n",
+    "\n",
+    "`LineaPy` can also create a Python module that includes several functions, and each function is responsible for calculating one artifact or common objects of multiple artifacts.\n",
+    "To be more specific, we've saved four artifacts(`raw_data`, `featured_data`, ` model`, `metric`) in our example workflow, and we might want to \n",
+    "1. change the `url`, so we can train the model with different data,\n",
+    "1. change the `import_module` and `model_name` so we can train different models,\n",
+    "1. change the `scoring` and `cv_scoring_metric_name` so we use a different metric to evaluate the model performance.\n",
+    "\n",
+    "These can be achieved by `lineapy.get_module` API as following"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "41sVKDSSb_X8",
+    "outputId": "cba35452-da35-46cd-d723-a87e8ecb1c4b"
+   },
+   "outputs": [],
+   "source": [
+    "helper_module = lineapy.get_module(\n",
+    "  [\"raw_data\",\"fe_data\",\"model\",\"model_metrics\"], \n",
+    "  input_parameters=[\"url\",\"import_module\",\"model_name\",\"scoring\",\"cv_scoring_metric_name\"],   \n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "BDIwxq0PWgIZ"
+   },
+   "source": [
+    "If we investigate objects within the module, we can see some function starts with *get_* and ends with the artifact name like"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "import argparse\n",
+      "import importlib\n",
+      "import importlib.util\n",
+      "from importlib.abc import Loader\n",
+      "\n",
+      "import pandas as pd\n",
+      "from sklearn.decomposition import PCA\n",
+      "from sklearn.ensemble import RandomForestClassifier\n",
+      "from sklearn.linear_model import SGDClassifier\n",
+      "from sklearn.metrics import confusion_matrix, f1_score, precision_score, recall_score\n",
+      "from sklearn.model_selection import cross_val_predict, cross_val_score, train_test_split\n",
+      "from sklearn.naive_bayes import GaussianNB\n",
+      "from sklearn.neighbors import KNeighborsClassifier\n",
+      "from sklearn.preprocessing import LabelEncoder, MinMaxScaler\n",
+      "from sklearn.svm import SVC\n",
+      "\n",
+      "\n",
+      "def get_raw_data(url):\n",
+      "    sdss_df = pd.read_csv(url)\n",
+      "    return sdss_df\n",
+      "\n",
+      "\n",
+      "def get_fe_data(sdss_df):\n",
+      "    sdss_df_fe = sdss_df.drop(\n",
+      "        [\"objid\", \"run\", \"rerun\", \"camcol\", \"field\", \"specobjid\"], axis=1\n",
+      "    )\n",
+      "    le = LabelEncoder()\n",
+      "    y_encoded = le.fit_transform(sdss_df_fe[\"class\"])\n",
+      "    sdss_df_fe[\"class\"] = y_encoded\n",
+      "    pca = PCA(n_components=3)\n",
+      "    ugriz = pca.fit_transform(sdss_df_fe[[\"u\", \"g\", \"r\", \"i\", \"z\"]])\n",
+      "    sdss_df_fe = pd.concat((sdss_df_fe, pd.DataFrame(ugriz)), axis=1)\n",
+      "    sdss_df_fe.rename({0: \"PCA_1\", 1: \"PCA_2\", 2: \"PCA_3\"}, axis=1, inplace=True)\n",
+      "    sdss_df_fe.drop([\"u\", \"g\", \"r\", \"i\", \"z\"], axis=1, inplace=True)\n",
+      "    return sdss_df_fe\n",
+      "\n",
+      "\n",
+      "def get_x_test_x_train_y_test_y_train_for_artifact_model_and_downstream(sdss_df_fe):\n",
+      "    scaler = MinMaxScaler()\n",
+      "    sdss = scaler.fit_transform(sdss_df_fe.drop(\"class\", axis=1))\n",
+      "    X_train, X_test, y_train, y_test = train_test_split(\n",
+      "        sdss, sdss_df_fe[\"class\"], test_size=0.33\n",
+      "    )\n",
+      "    return X_test, X_train, y_test, y_train\n",
+      "\n",
+      "\n",
+      "def get_model(X_train, import_module, model_name, y_train):\n",
+      "    def get_sklearn_model(import_module: str, model_name: str, model_params: dict):\n",
+      "        \"\"\"Returns a scikit-learn model\"\"\"\n",
+      "        model_class = getattr(importlib.import_module(import_module), model_name)\n",
+      "        model = model_class(**model_params)  # Instantiates the model\n",
+      "        return model\n",
+      "\n",
+      "    model_configuration = {}\n",
+      "    model = get_sklearn_model(import_module, model_name, model_configuration)\n",
+      "    model.fit(X_train, y_train)\n",
+      "    return model\n",
+      "\n",
+      "\n",
+      "def get_model_metrics(\n",
+      "    X_test, X_train, cv_scoring_metric_name, model, scoring, y_test, y_train\n",
+      "):\n",
+      "    preds = model.predict(X_test)\n",
+      "    acc = (preds == y_test).sum().astype(float) / len(preds) * 100\n",
+      "    cv_scores = cross_val_score(model, X_train, y_train, cv=10, scoring=scoring)\n",
+      "    cv_scoring_metric = getattr(cv_scores, cv_scoring_metric_name)()\n",
+      "    model_metrics = {\n",
+      "        scoring: acc,\n",
+      "        \"cv_scores\": cv_scores,\n",
+      "        \"cv_scoring_metric\": cv_scoring_metric,\n",
+      "    }\n",
+      "    return model_metrics\n",
+      "\n",
+      "\n",
+      "def run_session_including_raw_data(\n",
+      "    url=\"https://raw.githubusercontent.com/LineaLabs/lineapy/main/examples/use_cases/creating_reusable_components/data/Skyserver_SQL2_27_2018%206_51_39%20PM.csv\",\n",
+      "    import_module=\"sklearn.svm\",\n",
+      "    model_name=\"SVC\",\n",
+      "    scoring=\"accuracy\",\n",
+      "    cv_scoring_metric_name=\"mean\",\n",
+      "):\n",
+      "    # Given multiple artifacts, we need to save each right after\n",
+      "    # its calculation to protect from any irrelevant downstream\n",
+      "    # mutations (e.g., inside other artifact calculations)\n",
+      "    import copy\n",
+      "\n",
+      "    artifacts = dict()\n",
+      "    sdss_df = get_raw_data(url)\n",
+      "    artifacts[\"raw_data\"] = copy.deepcopy(sdss_df)\n",
+      "    sdss_df_fe = get_fe_data(sdss_df)\n",
+      "    artifacts[\"fe_data\"] = copy.deepcopy(sdss_df_fe)\n",
+      "    (\n",
+      "        X_test,\n",
+      "        X_train,\n",
+      "        y_test,\n",
+      "        y_train,\n",
+      "    ) = get_x_test_x_train_y_test_y_train_for_artifact_model_and_downstream(sdss_df_fe)\n",
+      "    model = get_model(X_train, import_module, model_name, y_train)\n",
+      "    artifacts[\"model\"] = copy.deepcopy(model)\n",
+      "    model_metrics = get_model_metrics(\n",
+      "        X_test, X_train, cv_scoring_metric_name, model, scoring, y_test, y_train\n",
+      "    )\n",
+      "    artifacts[\"model_metrics\"] = copy.deepcopy(model_metrics)\n",
+      "    return artifacts\n",
+      "\n",
+      "\n",
+      "def run_all_sessions(\n",
+      "    url=\"https://raw.githubusercontent.com/LineaLabs/lineapy/main/examples/use_cases/creating_reusable_components/data/Skyserver_SQL2_27_2018%206_51_39%20PM.csv\",\n",
+      "    import_module=\"sklearn.svm\",\n",
+      "    model_name=\"SVC\",\n",
+      "    scoring=\"accuracy\",\n",
+      "    cv_scoring_metric_name=\"mean\",\n",
+      "):\n",
+      "    artifacts = dict()\n",
+      "    artifacts.update(\n",
+      "        run_session_including_raw_data(\n",
+      "            url, import_module, model_name, scoring, cv_scoring_metric_name\n",
+      "        )\n",
+      "    )\n",
+      "    return artifacts\n",
+      "\n",
+      "\n",
+      "if __name__ == \"__main__\":\n",
+      "    # Edit this section to customize the behavior of artifacts\n",
+      "    parser = argparse.ArgumentParser()\n",
+      "    parser.add_argument(\n",
+      "        \"--url\",\n",
+      "        type=str,\n",
+      "        default=\"https://raw.githubusercontent.com/LineaLabs/lineapy/main/examples/use_cases/creating_reusable_components/data/Skyserver_SQL2_27_2018%206_51_39%20PM.csv\",\n",
+      "    )\n",
+      "    parser.add_argument(\"--import_module\", type=str, default=\"sklearn.svm\")\n",
+      "    parser.add_argument(\"--model_name\", type=str, default=\"SVC\")\n",
+      "    parser.add_argument(\"--scoring\", type=str, default=\"accuracy\")\n",
+      "    parser.add_argument(\"--cv_scoring_metric_name\", type=str, default=\"mean\")\n",
+      "    args = parser.parse_args()\n",
+      "    artifacts = run_all_sessions(\n",
+      "        url=args.url,\n",
+      "        import_module=args.import_module,\n",
+      "        model_name=args.model_name,\n",
+      "        scoring=args.scoring,\n",
+      "        cv_scoring_metric_name=args.cv_scoring_metric_name,\n",
+      "    )\n",
+      "    print(artifacts)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "helper_module_text = lineapy.get_module_definition(\n",
+    "  [\"raw_data\",\"fe_data\",\"model\",\"model_metrics\"], \n",
+    "  input_parameters=[\"url\",\"import_module\",\"model_name\",\"scoring\",\"cv_scoring_metric_name\"],   \n",
+    ")\n",
+    "print(helper_module_text)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ba5G4JDeVE2A",
+    "tags": []
+   },
+   "source": [
+    "#### What Is Happening Here?\n",
+    "\n",
+    "Dependent on the user-selected list of artifacts, `LineaPy` can divide one computation graph(a representation of your code) into multiple non-overlapping subgraphs. \n",
+    "Based on the user-selected list of input parameters, `LineaPy` can also parametrize the workflow.\n",
+    "\n",
+    "```\n",
+    "lineapy.get_module(\n",
+    "  [\"raw_data\",\"fe_data\",\"model\",\"model_metrics\"], \n",
+    "  input_parameters=[\"url\",\"import_module\",\"model_name\",\"scoring\",\"cv_scoring_metric_name\"],   \n",
+    ")\n",
+    "```\n",
+    "\n",
+    "In this case, we are expecting `get_raw_data()`, `get_fe_data()`, `get_model()`, and `get_model_metrics()` four functions, and each one corresponds to an artifact. \n",
+    "However, we can see one extra function, `get_x_test_x_train_y_test_y_train_for_artifact_model_and_downstream()`, shows up in the module file as well.\n",
+    "The reason is that `X_train` and `y_train` are used in both `get_model()` and `get_model_metrics()`; furthermore, `X_test`, `X_train`, `y_test` and `y_train` are created at the same statement.\n",
+    "Since `LineaPy` break up the code in a non-overlapping way; thus, we are seeing an extra `get_x_test_x_train_y_test_y_train_for_artifact_model_and_downstream()` function shows up here.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 708
+    },
+    "id": "n_K8oUceImbD",
+    "jupyter": {
+     "source_hidden": true
+    },
+    "outputId": "07e098c2-6a2f-41b6-fb73-80ee90186f36",
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x960 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "graph = \"\"\"\n",
+    "graph\n",
+    "\n",
+    "subgraph input\n",
+    "url[/url/]\n",
+    "im[/input_module/]\n",
+    "mn[/module_name/]\n",
+    "s[/scoring/]\n",
+    "csmn[/cv_scoring_metric_name/]\n",
+    "end \n",
+    "\n",
+    "subgraph workflow\n",
+    "GRD[\"get_raw_data()\"] \n",
+    "GFD[\"get_fe_data()\"]\n",
+    "GXXYY[\"get_x_test_x_train_y_test_y_train_for_artifact_model_and_downstream()\"]\n",
+    "GM[\"get_model()\"]\n",
+    "GMM[\"get_model_metrics()\"]\n",
+    "xtr[/X_train/]\n",
+    "ytr[/y_train/]\n",
+    "xte[/X_test/]\n",
+    "yte[/y_test/]\n",
+    "end\n",
+    "\n",
+    "subgraph output\n",
+    "rd[/raw_daga/]\n",
+    "fd[/fe_data/]\n",
+    "m[/model/]\n",
+    "mm[/model_metrics/]\n",
+    "end\n",
+    "\n",
+    "url --> GRD\n",
+    "GRD --> rd \n",
+    "rd --> GFD\n",
+    "GFD --> fd\n",
+    "fd --> GXXYY\n",
+    "GXXYY --> xtr & ytr & xte & yte\n",
+    "im & mn & xtr & ytr --> GM\n",
+    "GM --> m\n",
+    "xtr & ytr & xte & yte & csmn & s & m --> GMM\n",
+    "GMM --> mm\n",
+    "\"\"\"\n",
+    "\n",
+    "flowchart(graph, 240, 'Parameterized Workflow')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Reuse Functions within the Module\n",
+    "\n",
+    "After investigating all the function definitions within the module, we can reuse these functions individually to achieve more than the parameterization described in the previous example.\n",
+    "For instance, we want to use `keras` to train a three-layer dense neural network model with different dropout rates on the same training data set and features. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Reuse Functions to Load Data\n",
+    "\n",
+    "Here, we can reuse `get_raw_data()`, `get_fe_data()`, and `get_x_test_x_train_y_test_y_train_for_artifact_model_and_downstream()` to prepare the same training and validation data set as before."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "0LYr9cO6bn1C",
+    "outputId": "0f15835a-81c1-46e2-d10b-79bf403fdff6"
+   },
+   "outputs": [],
+   "source": [
+    "# Load raw data from artifact store\n",
+    "url = \"https://raw.githubusercontent.com/LineaLabs/lineapy/main/examples/use_cases/creating_reusable_components/data/Skyserver_SQL2_27_2018%206_51_39%20PM.csv\" \n",
+    "new_raw_data = helper_module.get_raw_data(url)\n",
+    "\n",
+    "# Do some tweak on the feature engineering \n",
+    "new_fe_data = helper_module.get_fe_data(new_raw_data)\n",
+    "new_X_test, new_X_train, new_y_test, new_y_train = helper_module.get_x_test_x_train_y_test_y_train_for_artifact_model_and_downstream(new_fe_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "xIMDhLSncE4l"
+   },
+   "source": [
+    "#### Train a Keras Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "0LYr9cO6bn1C",
+    "outputId": "0f15835a-81c1-46e2-d10b-79bf403fdff6"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2023-01-30 12:10:21.621262: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
+      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
+      "2023-01-30 12:10:21.740631: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
+      "2023-01-30 12:10:21.740648: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
+      "2023-01-30 12:10:22.332167: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n",
+      "2023-01-30 12:10:22.332234: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n",
+      "2023-01-30 12:10:22.332241: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n",
+      "2023-01-30 12:10:24.521028: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n",
+      "2023-01-30 12:10:24.521088: W tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:265] failed call to cuInit: UNKNOWN ERROR (303)\n",
+      "2023-01-30 12:10:24.521147: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (mjl001): /proc/driver/nvidia/version does not exist\n",
+      "2023-01-30 12:10:24.521746: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
+      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"sequential\"\n",
+      "_________________________________________________________________\n",
+      " Layer (type)                Output Shape              Param #   \n",
+      "=================================================================\n",
+      " flatten (Flatten)           (None, 9)                 0         \n",
+      "                                                                 \n",
+      " dense (Dense)               (None, 256)               2560      \n",
+      "                                                                 \n",
+      " dense_1 (Dense)             (None, 128)               32896     \n",
+      "                                                                 \n",
+      " dropout (Dropout)           (None, 128)               0         \n",
+      "                                                                 \n",
+      " dense_2 (Dense)             (None, 64)                8256      \n",
+      "                                                                 \n",
+      " dropout_1 (Dropout)         (None, 64)                0         \n",
+      "                                                                 \n",
+      " dense_3 (Dense)             (None, 32)                2080      \n",
+      "                                                                 \n",
+      " dropout_2 (Dropout)         (None, 32)                0         \n",
+      "                                                                 \n",
+      " dense_4 (Dense)             (None, 3)                 99        \n",
+      "                                                                 \n",
+      "=================================================================\n",
+      "Total params: 45,891\n",
+      "Trainable params: 45,891\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n",
+      "None\n",
+      "Keras weights file (<HDF5 file \"variables.h5\" (mode r+)>) saving:\n",
+      "...layers\n",
+      "......dense\n",
+      ".........vars\n",
+      "............0\n",
+      "............1\n",
+      "......dense_1\n",
+      ".........vars\n",
+      "............0\n",
+      "............1\n",
+      "......dense_2\n",
+      ".........vars\n",
+      "............0\n",
+      "............1\n",
+      "......dense_3\n",
+      ".........vars\n",
+      "............0\n",
+      "............1\n",
+      "......dense_4\n",
+      ".........vars\n",
+      "............0\n",
+      "............1\n",
+      "......dropout\n",
+      ".........vars\n",
+      "......dropout_1\n",
+      ".........vars\n",
+      "......dropout_2\n",
+      ".........vars\n",
+      "......flatten\n",
+      ".........vars\n",
+      "...metrics\n",
+      "......mean\n",
+      ".........vars\n",
+      "............0\n",
+      "............1\n",
+      "......mean_metric_wrapper\n",
+      ".........vars\n",
+      "............0\n",
+      "............1\n",
+      "...optimizer\n",
+      "......vars\n",
+      ".........0\n",
+      ".........1\n",
+      ".........10\n",
+      ".........2\n",
+      ".........3\n",
+      ".........4\n",
+      ".........5\n",
+      ".........6\n",
+      ".........7\n",
+      ".........8\n",
+      ".........9\n",
+      "...vars\n",
+      "Keras model archive saving:\n",
+      "File Name                                             Modified             Size\n",
+      "config.json                                    2023-01-30 12:10:29         3655\n",
+      "metadata.json                                  2023-01-30 12:10:29           64\n",
+      "variables.h5                                   2023-01-30 12:10:30       403480\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "LineaArtifact(name='nn_model', _version=8)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "import tensorflow as tf\n",
+    "from tensorflow import keras\n",
+    "import lineapy\n",
+    "\n",
+    "# Here are we are not using cross validation but using a hold out set for validation during the training\n",
+    "XTrain, XVal, YTrain, YVal = train_test_split(new_X_train, new_y_train, test_size = 0.1, random_state=101)\n",
+    "\n",
+    "# We might want to tune the dropout rate between different layer \n",
+    "dropout1 = 0.25\n",
+    "dropout2 = 0.25\n",
+    "dropout3 = 0.5\n",
+    "\n",
+    "# Construct the model\n",
+    "kmodel = keras.models.Sequential([\n",
+    "    keras.layers.Flatten(),\n",
+    "    keras.layers.Dense(256, activation='relu'),\n",
+    "    keras.layers.Dense(128, activation='relu'),\n",
+    "    keras.layers.Dropout(dropout1),\n",
+    "    keras.layers.Dense(64, activation='relu'),\n",
+    "    keras.layers.Dropout(dropout2),\n",
+    "    keras.layers.Dense(32, activation='relu'),\n",
+    "    keras.layers.Dropout(dropout3),\n",
+    "    keras.layers.Dense(3, activation='softmax')\n",
+    "])\n",
+    "# Set opimizer\n",
+    "optimizer = tf.keras.optimizers.RMSprop()\n",
+    "# Compile the model\n",
+    "kmodel.compile(optimizer=optimizer , loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
+    "# Set training parameters\n",
+    "learning_rate_reduction = keras.callbacks.ReduceLROnPlateau(\n",
+    "    monitor='accuracy', \n",
+    "    patience=3, \n",
+    "    verbose=1, \n",
+    "    factor=0.5, \n",
+    "    min_lr=0.00001)\n",
+    "# Train the model\n",
+    "history = kmodel.fit(\n",
+    "    XTrain, YTrain,\n",
+    "    batch_size=86,\n",
+    "    epochs = 30,\n",
+    "    validation_data = (XVal,YVal),\n",
+    "    verbose=0,\n",
+    "    callbacks=[learning_rate_reduction]\n",
+    ")\n",
+    "\n",
+    "print(kmodel.summary())\n",
+    "\n",
+    "# Save trained model as artifact\n",
+    "lineapy.save(kmodel, 'nn_model')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ENOeYjJSixqq"
+   },
+   "source": [
+    "#### Evaluate the model performance\n",
+    "\n",
+    "Now we can evaluate our neural network model performance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "104/104 [==============================] - 0s 696us/step\n",
+      "104/104 [==============================] - 0s 835us/step - loss: 0.0923 - accuracy: 0.9755\n",
+      "[0.09232450276613235, 0.975454568862915]\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "results = kmodel.predict(X_test)\n",
+    "evaluation_result = kmodel.evaluate(X_test, y_test)\n",
+    "\n",
+    "# Save metric as an artifact\n",
+    "art = lineapy.save(evaluation_result, 'nn_model_evaluation')\n",
+    "print(evaluation_result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "c1ec976a-51d8-4bb2-b93b-bdf30981f277",
+    "_uuid": "87c2b7276cbe466417b8f696c93dc7055a3454ae",
+    "id": "3trrVRCRQFMw"
+   },
+   "source": [
+    "## Recap\n",
+    "\n",
+    "In this tutorial, we've \n",
+    "1. used a machine learning model developing example to demonstrate how to save artifacts as checkpoints and create reusable functions and modules to reuse the workflow,\n",
+    "1. explained how these reusable components are created\n",
+    "1. demonstrated how to use these reusable components in two different ways.\n",
+    "\n",
+    "These reusable components can help individual users with\n",
+    "1. organizing their development and experiments\n",
+    "\n",
+    "These reusable components can help organizations with\n",
+    "1. sharing work between teammates\n",
+    "1. deploying standardized components within the organization\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## What Next"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"admonition info\" style=\"background:#F0FAFA\">\n",
+    "    <p class=\"admonition-title\"><b>Info</b></p>\n",
+    "    <p>\n",
+    "        If you want to learn more about LineaPy's pipeline support, check out the project <a href=\"https://docs.lineapy.org/latest/guides/usage/pipeline-building/\">documentation</a>.\n",
+    "    </p>\n",
+    "</div>"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "collapsed_sections": [
+    "c0cVXS1nbaFc"
+   ],
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "cacea137424fd3104c427a06c692d13a169698dd1bd116af82859e9688265e62"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/examples/tutorials/03_reusable_components.ipynb b/examples/tutorials/03_reusable_components.ipynb
new file mode 100644
index 000000000..73be695d5
--- /dev/null
+++ b/examples/tutorials/03_reusable_components.ipynb
@@ -0,0 +1,1845 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "fa3d2a9c-442b-44c2-bf4d-45bb544ec369",
+    "_uuid": "bcba1675eebc008a35c21f6b64174161ff7d4c48",
+    "id": "4a1bScthQFMl"
+   },
+   "source": [
+    "# Reusable Components with LineaPy\n",
+    "\n",
+    "This tutorial will use a typical data science workflow, including the following steps.\n",
+    "\n",
+    "1. reading raw data\n",
+    "1. doing exploratory data analysis\n",
+    "1. doing feature engineering\n",
+    "1. training an ML model\n",
+    "1. evaluating model performance\n",
+    "\n",
+    "As our example to demonstrate how to use `LineaPy` to create reusable components and how we can reuse these components."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "i-i9q7mtPGHZ",
+    "tags": []
+   },
+   "source": [
+    "## Part 1: Go Through a Typical Data Scientist Workflow\n",
+    "\n",
+    "Let's use [Sloan Digital Sky Survey Classification](https://www.kaggle.com/datasets/lucidlenn/sloan-digital-sky-survey) as our example. \n",
+    "The data consists of 10,000 observations of space taken by the [SDSS](http://www.sdss.org/dr14/). \n",
+    "Every observation is described by 17 feature columns and 1 class column, identifying it as a star, galaxy, or quasar.\n",
+    "Our example is training a model to predict the object class(galaxy, star, or quasar) based on the 17 features.\n",
+    "\n",
+    "Detailed dataset descriptions are available at [Sloan Digital Sky Survey Classification](https://www.kaggle.com/datasets/lucidlenn/sloan-digital-sky-survey).\n",
+    "More background education about galaxies is available at [UMD ASTR620 class](https://www.astro.umd.edu/~richard/ASTRO620/index_fall2015.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "b9d4b609-9f0f-4296-8462-98975b2ece09",
+    "_uuid": "16d228ddb3b0d71d6e13093552c04a21146b75e5",
+    "id": "IeFWMBAZQFMo",
+    "tags": []
+   },
+   "source": [
+    "### Importing Libraries and Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#NBVAL_SKIP\n",
+    "!pip -q install lineapy~=0.2 scikit-learn pandas matplotlib seaborn numpy tensorflow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "w0H6N-TzQQ94",
+    "outputId": "3cdb6e4d-fbfc-4143-8378-6f91a0080237"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mingjerli/miniconda3/envs/lineapy39/lib/python3.9/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "#NBVAL_SKIP\n",
+    "%load_ext lineapy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "_cell_guid": "13914766-c2fb-4801-8846-6c78e6d1cb03",
+    "_uuid": "5bb212bdb34abc34f8bed1a0bc2d1a6287166221",
+    "id": "Yz8i8KUNQFMp"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib import style\n",
+    "import seaborn as sns\n",
+    "sns.set_style('whitegrid')\n",
+    "from sklearn.model_selection import train_test_split, cross_val_predict\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.linear_model import SGDClassifier\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "from sklearn.decomposition import PCA\n",
+    "from sklearn.preprocessing import LabelEncoder, MinMaxScaler\n",
+    "from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score\n",
+    "import time\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n",
+    "\n",
+    "import importlib\n",
+    "import sklearn\n",
+    "import importlib.util\n",
+    "import sys\n",
+    "import tempfile\n",
+    "from importlib.abc import Loader\n",
+    "from pathlib import Path\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "SMALL_SIZE = 10\n",
+    "MEDIUM_SIZE = 12\n",
+    "\n",
+    "plt.rcParams['figure.dpi']=240"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "id": "sP_le67aWA8n"
+   },
+   "outputs": [],
+   "source": [
+    "def flowchart(graph, dpi = 240, title=\"\"):\n",
+    "    \"\"\"\n",
+    "    Draw mermaid diagram in notebook\n",
+    "    \n",
+    "    We use this to visualize some diagrams in the rest of the  notebook, it's not part of LineaPy\n",
+    "    \"\"\"\n",
+    "    import base64\n",
+    "    import requests, io\n",
+    "    from PIL import Image\n",
+    "    import matplotlib.pyplot as plt\n",
+    "\n",
+    "    graphbytes = graph.encode(\"ascii\")\n",
+    "    base64_bytes = base64.b64encode(graphbytes)\n",
+    "    base64_string = base64_bytes.decode(\"ascii\")\n",
+    "    img = Image.open(io.BytesIO(requests.get('https://mermaid.ink/img/' + base64_string).content))\n",
+    "    plt.rcParams['figure.dpi']=dpi\n",
+    "    plt.imshow(img)\n",
+    "    plt.grid(None)\n",
+    "    plt.xticks([])\n",
+    "    plt.yticks([])\n",
+    "    plt.title(title)          "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "AGgT4L23Nilj",
+    "tags": []
+   },
+   "source": [
+    "### Load Dataset\n",
+    "\n",
+    "Load Data and Save Artifact as CheckPoint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "_cell_guid": "ffb06ef6-73f7-4f42-ab42-5d5b5f773ba7",
+    "_uuid": "04e88f8c9c12167a1c23e47b3e2046246510e983",
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "-xtqrprxQFMr",
+    "outputId": "afd4955d-d2f1-4676-c79b-147eae0ee19b"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LineaArtifact(name='raw_data', _version=10)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# NBVAL_IGNORE_OUTPUT\n",
+    "url = \"https://raw.githubusercontent.com/LineaLabs/lineapy/main/examples/use_cases/creating_reusable_components/data/Skyserver_SQL2_27_2018%206_51_39%20PM.csv\"\n",
+    "sdss_df = pd.read_csv(url)\n",
+    "lineapy.save(sdss_df, 'raw_data')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "75a847f1-0dfc-4228-9cbc-49d6034463e5",
+    "_uuid": "9b01bc847e158cfa00d411ea687cb573a0037aef",
+    "id": "hyR_FbSwQFMs",
+    "tags": []
+   },
+   "source": [
+    "### Explorer the Dataset\n",
+    "\n",
+    "The dataset has 10000 examples, 17 feature columns, and one target column. 8 of the 17 features are 64-bit integers, one feature is an unsigned 64-bit integer, 8 are 64-bit floats, and the target column is of the type object. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 270
+    },
+    "id": "4Je22tYwPqF-",
+    "outputId": "67929a67-7ebb-4e8f-8d26-b1989ad66c70"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>objid</th>\n",
+       "      <th>ra</th>\n",
+       "      <th>dec</th>\n",
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+       "      <th>g</th>\n",
+       "      <th>r</th>\n",
+       "      <th>i</th>\n",
+       "      <th>z</th>\n",
+       "      <th>run</th>\n",
+       "      <th>rerun</th>\n",
+       "      <th>camcol</th>\n",
+       "      <th>field</th>\n",
+       "      <th>specobjid</th>\n",
+       "      <th>class</th>\n",
+       "      <th>redshift</th>\n",
+       "      <th>plate</th>\n",
+       "      <th>mjd</th>\n",
+       "      <th>fiberid</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.237650e+18</td>\n",
+       "      <td>183.531326</td>\n",
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+       "      <td>17.04240</td>\n",
+       "      <td>15.94699</td>\n",
+       "      <td>15.50342</td>\n",
+       "      <td>15.22531</td>\n",
+       "      <td>752</td>\n",
+       "      <td>301</td>\n",
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+       "      <td>3.722360e+18</td>\n",
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+       "      <td>-0.000009</td>\n",
+       "      <td>3306</td>\n",
+       "      <td>54922</td>\n",
+       "      <td>491</td>\n",
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+       "      <th>1</th>\n",
+       "      <td>1.237650e+18</td>\n",
+       "      <td>183.598370</td>\n",
+       "      <td>0.135285</td>\n",
+       "      <td>18.66280</td>\n",
+       "      <td>17.21449</td>\n",
+       "      <td>16.67637</td>\n",
+       "      <td>16.48922</td>\n",
+       "      <td>16.39150</td>\n",
+       "      <td>752</td>\n",
+       "      <td>301</td>\n",
+       "      <td>4</td>\n",
+       "      <td>267</td>\n",
+       "      <td>3.638140e+17</td>\n",
+       "      <td>STAR</td>\n",
+       "      <td>-0.000055</td>\n",
+       "      <td>323</td>\n",
+       "      <td>51615</td>\n",
+       "      <td>541</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.237650e+18</td>\n",
+       "      <td>183.680207</td>\n",
+       "      <td>0.126185</td>\n",
+       "      <td>19.38298</td>\n",
+       "      <td>18.19169</td>\n",
+       "      <td>17.47428</td>\n",
+       "      <td>17.08732</td>\n",
+       "      <td>16.80125</td>\n",
+       "      <td>752</td>\n",
+       "      <td>301</td>\n",
+       "      <td>4</td>\n",
+       "      <td>268</td>\n",
+       "      <td>3.232740e+17</td>\n",
+       "      <td>GALAXY</td>\n",
+       "      <td>0.123111</td>\n",
+       "      <td>287</td>\n",
+       "      <td>52023</td>\n",
+       "      <td>513</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.237650e+18</td>\n",
+       "      <td>183.870529</td>\n",
+       "      <td>0.049911</td>\n",
+       "      <td>17.76536</td>\n",
+       "      <td>16.60272</td>\n",
+       "      <td>16.16116</td>\n",
+       "      <td>15.98233</td>\n",
+       "      <td>15.90438</td>\n",
+       "      <td>752</td>\n",
+       "      <td>301</td>\n",
+       "      <td>4</td>\n",
+       "      <td>269</td>\n",
+       "      <td>3.722370e+18</td>\n",
+       "      <td>STAR</td>\n",
+       "      <td>-0.000111</td>\n",
+       "      <td>3306</td>\n",
+       "      <td>54922</td>\n",
+       "      <td>510</td>\n",
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+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.237650e+18</td>\n",
+       "      <td>183.883288</td>\n",
+       "      <td>0.102557</td>\n",
+       "      <td>17.55025</td>\n",
+       "      <td>16.26342</td>\n",
+       "      <td>16.43869</td>\n",
+       "      <td>16.55492</td>\n",
+       "      <td>16.61326</td>\n",
+       "      <td>752</td>\n",
+       "      <td>301</td>\n",
+       "      <td>4</td>\n",
+       "      <td>269</td>\n",
+       "      <td>3.722370e+18</td>\n",
+       "      <td>STAR</td>\n",
+       "      <td>0.000590</td>\n",
+       "      <td>3306</td>\n",
+       "      <td>54922</td>\n",
+       "      <td>512</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          objid          ra       dec         u         g         r         i  \\\n",
+       "0  1.237650e+18  183.531326  0.089693  19.47406  17.04240  15.94699  15.50342   \n",
+       "1  1.237650e+18  183.598370  0.135285  18.66280  17.21449  16.67637  16.48922   \n",
+       "2  1.237650e+18  183.680207  0.126185  19.38298  18.19169  17.47428  17.08732   \n",
+       "3  1.237650e+18  183.870529  0.049911  17.76536  16.60272  16.16116  15.98233   \n",
+       "4  1.237650e+18  183.883288  0.102557  17.55025  16.26342  16.43869  16.55492   \n",
+       "\n",
+       "          z  run  rerun  camcol  field     specobjid   class  redshift  plate  \\\n",
+       "0  15.22531  752    301       4    267  3.722360e+18    STAR -0.000009   3306   \n",
+       "1  16.39150  752    301       4    267  3.638140e+17    STAR -0.000055    323   \n",
+       "2  16.80125  752    301       4    268  3.232740e+17  GALAXY  0.123111    287   \n",
+       "3  15.90438  752    301       4    269  3.722370e+18    STAR -0.000111   3306   \n",
+       "4  16.61326  752    301       4    269  3.722370e+18    STAR  0.000590   3306   \n",
+       "\n",
+       "     mjd  fiberid  \n",
+       "0  54922      491  \n",
+       "1  51615      541  \n",
+       "2  52023      513  \n",
+       "3  54922      510  \n",
+       "4  54922      512  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# NBVAL_IGNORE_OUTPUT\n",
+    "sdss_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "_cell_guid": "6723c745-8446-46f0-a866-8c22668607d3",
+    "_uuid": "77495f8526975b41e2ba43063b82d807e8ba1109",
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 364
+    },
+    "id": "zLigzi31QFMs",
+    "outputId": "d9dd7b32-7e54-42cb-a300-f6b86ecd7028",
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>objid</th>\n",
+       "      <th>ra</th>\n",
+       "      <th>dec</th>\n",
+       "      <th>u</th>\n",
+       "      <th>g</th>\n",
+       "      <th>r</th>\n",
+       "      <th>i</th>\n",
+       "      <th>z</th>\n",
+       "      <th>run</th>\n",
+       "      <th>rerun</th>\n",
+       "      <th>camcol</th>\n",
+       "      <th>field</th>\n",
+       "      <th>specobjid</th>\n",
+       "      <th>redshift</th>\n",
+       "      <th>plate</th>\n",
+       "      <th>mjd</th>\n",
+       "      <th>fiberid</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>1.000000e+04</td>\n",
+       "      <td>10000.000000</td>\n",
+       "      <td>10000.000000</td>\n",
+       "      <td>10000.000000</td>\n",
+       "      <td>10000.000000</td>\n",
+       "      <td>10000.000000</td>\n",
+       "      <td>10000.000000</td>\n",
+       "      <td>10000.000000</td>\n",
+       "      <td>10000.000000</td>\n",
+       "      <td>10000.0</td>\n",
+       "      <td>10000.000000</td>\n",
+       "      <td>10000.000000</td>\n",
+       "      <td>1.000000e+04</td>\n",
+       "      <td>10000.000000</td>\n",
+       "      <td>10000.000000</td>\n",
+       "      <td>10000.000000</td>\n",
+       "      <td>10000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>1.237650e+18</td>\n",
+       "      <td>175.529987</td>\n",
+       "      <td>14.836148</td>\n",
+       "      <td>18.619355</td>\n",
+       "      <td>17.371931</td>\n",
+       "      <td>16.840963</td>\n",
+       "      <td>16.583579</td>\n",
+       "      <td>16.422833</td>\n",
+       "      <td>981.034800</td>\n",
+       "      <td>301.0</td>\n",
+       "      <td>3.648700</td>\n",
+       "      <td>302.380100</td>\n",
+       "      <td>1.645022e+18</td>\n",
+       "      <td>0.143726</td>\n",
+       "      <td>1460.986400</td>\n",
+       "      <td>52943.533300</td>\n",
+       "      <td>353.069400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>47.783439</td>\n",
+       "      <td>25.212207</td>\n",
+       "      <td>0.828656</td>\n",
+       "      <td>0.945457</td>\n",
+       "      <td>1.067764</td>\n",
+       "      <td>1.141805</td>\n",
+       "      <td>1.203188</td>\n",
+       "      <td>273.305024</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.666183</td>\n",
+       "      <td>162.577763</td>\n",
+       "      <td>2.013998e+18</td>\n",
+       "      <td>0.388774</td>\n",
+       "      <td>1788.778371</td>\n",
+       "      <td>1511.150651</td>\n",
+       "      <td>206.298149</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.237650e+18</td>\n",
+       "      <td>8.235100</td>\n",
+       "      <td>-5.382632</td>\n",
+       "      <td>12.988970</td>\n",
+       "      <td>12.799550</td>\n",
+       "      <td>12.431600</td>\n",
+       "      <td>11.947210</td>\n",
+       "      <td>11.610410</td>\n",
+       "      <td>308.000000</td>\n",
+       "      <td>301.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>2.995780e+17</td>\n",
+       "      <td>-0.004136</td>\n",
+       "      <td>266.000000</td>\n",
+       "      <td>51578.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>1.237650e+18</td>\n",
+       "      <td>157.370946</td>\n",
+       "      <td>-0.539035</td>\n",
+       "      <td>18.178035</td>\n",
+       "      <td>16.815100</td>\n",
+       "      <td>16.173333</td>\n",
+       "      <td>15.853705</td>\n",
+       "      <td>15.618285</td>\n",
+       "      <td>752.000000</td>\n",
+       "      <td>301.0</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>184.000000</td>\n",
+       "      <td>3.389248e+17</td>\n",
+       "      <td>0.000081</td>\n",
+       "      <td>301.000000</td>\n",
+       "      <td>51900.000000</td>\n",
+       "      <td>186.750000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>1.237650e+18</td>\n",
+       "      <td>180.394514</td>\n",
+       "      <td>0.404166</td>\n",
+       "      <td>18.853095</td>\n",
+       "      <td>17.495135</td>\n",
+       "      <td>16.858770</td>\n",
+       "      <td>16.554985</td>\n",
+       "      <td>16.389945</td>\n",
+       "      <td>756.000000</td>\n",
+       "      <td>301.0</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>299.000000</td>\n",
+       "      <td>4.966580e+17</td>\n",
+       "      <td>0.042591</td>\n",
+       "      <td>441.000000</td>\n",
+       "      <td>51997.000000</td>\n",
+       "      <td>351.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>1.237650e+18</td>\n",
+       "      <td>201.547279</td>\n",
+       "      <td>35.649397</td>\n",
+       "      <td>19.259232</td>\n",
+       "      <td>18.010145</td>\n",
+       "      <td>17.512675</td>\n",
+       "      <td>17.258550</td>\n",
+       "      <td>17.141447</td>\n",
+       "      <td>1331.000000</td>\n",
+       "      <td>301.0</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>414.000000</td>\n",
+       "      <td>2.881300e+18</td>\n",
+       "      <td>0.092579</td>\n",
+       "      <td>2559.000000</td>\n",
+       "      <td>54468.000000</td>\n",
+       "      <td>510.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>1.237650e+18</td>\n",
+       "      <td>260.884382</td>\n",
+       "      <td>68.542265</td>\n",
+       "      <td>19.599900</td>\n",
+       "      <td>19.918970</td>\n",
+       "      <td>24.802040</td>\n",
+       "      <td>28.179630</td>\n",
+       "      <td>22.833060</td>\n",
+       "      <td>1412.000000</td>\n",
+       "      <td>301.0</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>768.000000</td>\n",
+       "      <td>9.468830e+18</td>\n",
+       "      <td>5.353854</td>\n",
+       "      <td>8410.000000</td>\n",
+       "      <td>57481.000000</td>\n",
+       "      <td>1000.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              objid            ra           dec             u             g  \\\n",
+       "count  1.000000e+04  10000.000000  10000.000000  10000.000000  10000.000000   \n",
+       "mean   1.237650e+18    175.529987     14.836148     18.619355     17.371931   \n",
+       "std    0.000000e+00     47.783439     25.212207      0.828656      0.945457   \n",
+       "min    1.237650e+18      8.235100     -5.382632     12.988970     12.799550   \n",
+       "25%    1.237650e+18    157.370946     -0.539035     18.178035     16.815100   \n",
+       "50%    1.237650e+18    180.394514      0.404166     18.853095     17.495135   \n",
+       "75%    1.237650e+18    201.547279     35.649397     19.259232     18.010145   \n",
+       "max    1.237650e+18    260.884382     68.542265     19.599900     19.918970   \n",
+       "\n",
+       "                  r             i             z           run    rerun  \\\n",
+       "count  10000.000000  10000.000000  10000.000000  10000.000000  10000.0   \n",
+       "mean      16.840963     16.583579     16.422833    981.034800    301.0   \n",
+       "std        1.067764      1.141805      1.203188    273.305024      0.0   \n",
+       "min       12.431600     11.947210     11.610410    308.000000    301.0   \n",
+       "25%       16.173333     15.853705     15.618285    752.000000    301.0   \n",
+       "50%       16.858770     16.554985     16.389945    756.000000    301.0   \n",
+       "75%       17.512675     17.258550     17.141447   1331.000000    301.0   \n",
+       "max       24.802040     28.179630     22.833060   1412.000000    301.0   \n",
+       "\n",
+       "             camcol         field     specobjid      redshift         plate  \\\n",
+       "count  10000.000000  10000.000000  1.000000e+04  10000.000000  10000.000000   \n",
+       "mean       3.648700    302.380100  1.645022e+18      0.143726   1460.986400   \n",
+       "std        1.666183    162.577763  2.013998e+18      0.388774   1788.778371   \n",
+       "min        1.000000     11.000000  2.995780e+17     -0.004136    266.000000   \n",
+       "25%        2.000000    184.000000  3.389248e+17      0.000081    301.000000   \n",
+       "50%        4.000000    299.000000  4.966580e+17      0.042591    441.000000   \n",
+       "75%        5.000000    414.000000  2.881300e+18      0.092579   2559.000000   \n",
+       "max        6.000000    768.000000  9.468830e+18      5.353854   8410.000000   \n",
+       "\n",
+       "                mjd       fiberid  \n",
+       "count  10000.000000  10000.000000  \n",
+       "mean   52943.533300    353.069400  \n",
+       "std     1511.150651    206.298149  \n",
+       "min    51578.000000      1.000000  \n",
+       "25%    51900.000000    186.750000  \n",
+       "50%    51997.000000    351.000000  \n",
+       "75%    54468.000000    510.000000  \n",
+       "max    57481.000000   1000.000000  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# NBVAL_IGNORE_OUTPUT\n",
+    "sdss_df.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "10fc6f0d-fad0-44ca-8996-bdf513e48358",
+    "_uuid": "2fe1eb4a169a3400d65c3f5d3134ae8fb250240e",
+    "id": "5Fyi0tHiQFMu",
+    "tags": []
+   },
+   "source": [
+    "### Feature Engineering\n",
+    "\n",
+    "#### u, g, r, i, z\n",
+    "\n",
+    "We will now reduce the number of dimensions by replacing the different bands 'u,' 'g,' 'r,' 'i', and 'z' with a linear combination with only three dimensions using **Principal Component Analysis**.\n",
+    "\n",
+    "**Principal Component Analysis:**\n",
+    "\n",
+    "n observations with p features can be interpreted as n points in p-dimensional space. PCA aims to project this space into a q-dimensional subspace (with q<p) with as little information loss as possible. \n",
+    "\n",
+    "It finds the q directions in which the n points vary the most (--> the principal components). It then projects the original data points into the q-dimensional subspace. PCA returns a n x q dimensional matrix. \n",
+    "\n",
+    "Using PCA on our data will decrease the number of operations during training and testing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "_cell_guid": "fbb45762-272f-40b8-bde6-9d3dd9c1cd55",
+    "_uuid": "8a97dca248a7b0473c784af669ea00b59017fa8a",
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 206
+    },
+    "id": "hjP6UjNjQFMu",
+    "outputId": "390e7b44-99a4-452d-f111-909f13724971"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ra</th>\n",
+       "      <th>dec</th>\n",
+       "      <th>class</th>\n",
+       "      <th>redshift</th>\n",
+       "      <th>plate</th>\n",
+       "      <th>mjd</th>\n",
+       "      <th>fiberid</th>\n",
+       "      <th>PCA_1</th>\n",
+       "      <th>PCA_2</th>\n",
+       "      <th>PCA_3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>183.531326</td>\n",
+       "      <td>0.089693</td>\n",
+       "      <td>2</td>\n",
+       "      <td>-0.000009</td>\n",
+       "      <td>3306</td>\n",
+       "      <td>54922</td>\n",
+       "      <td>491</td>\n",
+       "      <td>-1.507202</td>\n",
+       "      <td>-1.377293</td>\n",
+       "      <td>-0.265119</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>183.598370</td>\n",
+       "      <td>0.135285</td>\n",
+       "      <td>2</td>\n",
+       "      <td>-0.000055</td>\n",
+       "      <td>323</td>\n",
+       "      <td>51615</td>\n",
+       "      <td>541</td>\n",
+       "      <td>-0.195758</td>\n",
+       "      <td>-0.028410</td>\n",
+       "      <td>-0.155695</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>183.680207</td>\n",
+       "      <td>0.126185</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.123111</td>\n",
+       "      <td>287</td>\n",
+       "      <td>52023</td>\n",
+       "      <td>513</td>\n",
+       "      <td>1.297604</td>\n",
+       "      <td>-0.590023</td>\n",
+       "      <td>0.140338</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>183.870529</td>\n",
+       "      <td>0.049911</td>\n",
+       "      <td>2</td>\n",
+       "      <td>-0.000111</td>\n",
+       "      <td>3306</td>\n",
+       "      <td>54922</td>\n",
+       "      <td>510</td>\n",
+       "      <td>-1.446117</td>\n",
+       "      <td>0.566685</td>\n",
+       "      <td>-0.009272</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>183.883288</td>\n",
+       "      <td>0.102557</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.000590</td>\n",
+       "      <td>3306</td>\n",
+       "      <td>54922</td>\n",
+       "      <td>512</td>\n",
+       "      <td>-0.849271</td>\n",
+       "      <td>1.287505</td>\n",
+       "      <td>-0.397689</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           ra       dec  class  redshift  plate    mjd  fiberid     PCA_1  \\\n",
+       "0  183.531326  0.089693      2 -0.000009   3306  54922      491 -1.507202   \n",
+       "1  183.598370  0.135285      2 -0.000055    323  51615      541 -0.195758   \n",
+       "2  183.680207  0.126185      0  0.123111    287  52023      513  1.297604   \n",
+       "3  183.870529  0.049911      2 -0.000111   3306  54922      510 -1.446117   \n",
+       "4  183.883288  0.102557      2  0.000590   3306  54922      512 -0.849271   \n",
+       "\n",
+       "      PCA_2     PCA_3  \n",
+       "0 -1.377293 -0.265119  \n",
+       "1 -0.028410 -0.155695  \n",
+       "2 -0.590023  0.140338  \n",
+       "3  0.566685 -0.009272  \n",
+       "4  1.287505 -0.397689  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# NBVAL_IGNORE_OUTPUT\n",
+    "\n",
+    "# Keep only feature and target columns\n",
+    "sdss_df_fe = sdss_df.drop(['objid', 'run', 'rerun', 'camcol', 'field', 'specobjid'], axis=1)\n",
+    "\n",
+    "# encode class labels to integers\n",
+    "le = LabelEncoder()\n",
+    "y_encoded = le.fit_transform(sdss_df_fe['class'])\n",
+    "sdss_df_fe['class'] = y_encoded\n",
+    "\n",
+    "# Principal Component Analysis\n",
+    "pca = PCA(n_components=3)\n",
+    "ugriz = pca.fit_transform(sdss_df_fe[['u', 'g', 'r', 'i', 'z']])\n",
+    "\n",
+    "# update dataframe \n",
+    "sdss_df_fe = pd.concat((sdss_df_fe, pd.DataFrame(ugriz)), axis=1)\n",
+    "sdss_df_fe.rename({0: 'PCA_1', 1: 'PCA_2', 2: 'PCA_3'}, axis=1, inplace = True)\n",
+    "sdss_df_fe.drop(['u', 'g', 'r', 'i', 'z'], axis=1, inplace=True)\n",
+    "\n",
+    "# Save artifact and register checkpoint\n",
+    "lineapy.save(sdss_df_fe, 'fe_data')\n",
+    "sdss_df_fe.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "4e797850-cbb6-44f8-84d8-2649d9dcb461",
+    "_uuid": "73423e72a3e388e8d6b8f16672a7b77215359cfe",
+    "id": "SxwYeo_jQFMu",
+    "tags": []
+   },
+   "source": [
+    "### Machine Learning Models - Training"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "PS9EP1qyOJn6"
+   },
+   "source": [
+    "#### Feature Scaling\n",
+    "\n",
+    "We will now train different models on this dataset. \n",
+    "Scaling all values to be within the (0, 1) interval will reduce the distortion due to exceptionally high values and make some algorithms converge faster."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "_cell_guid": "23fc8398-331d-4164-8779-0516264ece29",
+    "_uuid": "c147b9e899cc2dd508d736274c856f88fb49321d",
+    "id": "33WC-SSjQFMu"
+   },
+   "outputs": [],
+   "source": [
+    "scaler = MinMaxScaler()\n",
+    "sdss = scaler.fit_transform(sdss_df_fe.drop('class', axis=1))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "13e1fd6f-820c-4098-a486-0079b300e6c9",
+    "_uuid": "e76e964a4b93c27e1ab01c24a06be09d8b304970",
+    "id": "MZlx5sj6QFMu"
+   },
+   "source": [
+    "#### Train Test Split\n",
+    "We will split the data into a training and a test part and use the training set for training the model and the test set for validation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "id": "Yf_VKm0o_utP"
+   },
+   "outputs": [],
+   "source": [
+    " X_train, X_test, y_train, y_test= train_test_split(sdss, sdss_df_fe['class'], test_size=0.33)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Xz0znPYjQFMu"
+   },
+   "source": [
+    "#### Train Model\n",
+    "\n",
+    "Here, we are training an SVC model. Note that instead of using `from sklearn.svm import SVC` to initiate the model instance, we wrote a general wrapper to create any `sklearn` model by name for reusability purposes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "TVS7WN-ruqP2",
+    "outputId": "85d0bf02-2834-4bd4-945e-f472a67a231c"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LineaArtifact(name='model', _version=9)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# NBVAL_IGNORE_OUTPUT\n",
+    "def get_sklearn_model(\n",
+    "    import_module: str, model_name: str, model_params: dict\n",
+    "):\n",
+    "    \"\"\"Returns a scikit-learn model\"\"\"\n",
+    "    model_class = getattr(importlib.import_module(import_module), model_name)\n",
+    "    model = model_class(**model_params)  # Instantiates the model\n",
+    "    return model\n",
+    "\n",
+    "import_module = \"sklearn.svm\"\n",
+    "model_name = \"SVC\"\n",
+    "model_configuration = {}\n",
+    "model = get_sklearn_model(import_module, model_name, model_configuration)\n",
+    "\n",
+    "# Train the model\n",
+    "model.fit(X_train, y_train)\n",
+    "\n",
+    "# Save model as artifact for checkpoint\n",
+    "lineapy.save(model, \"model\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "9jQsjqWCOjfN",
+    "tags": []
+   },
+   "source": [
+    "### Evaluate Model performance\n",
+    "\n",
+    "Here, we perform k-fold cross-validation to get a more realistic result by testing the performance for ten different train and test datasets and averaging the results. Cross-validation ensures that the above result is not arbitrary and gives a more reliable performance check."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "hVA4arodHglE",
+    "outputId": "4c1fc5f0-4186-4a99-f42a-7848d6a81e3c"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'accuracy': 93.6969696969697, 'cv_scores': array([0.95223881, 0.9238806 , 0.94477612, 0.94477612, 0.94626866,\n",
+      "       0.94626866, 0.95223881, 0.93432836, 0.94029851, 0.93432836]), 'cv_scoring_metric': 0.9419402985074627}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# NBVAL_IGNORE_OUTPUT\n",
+    "# Evaluating the Model Performance\n",
+    "\n",
+    "preds = model.predict(X_test)\n",
+    "# model accuracies\n",
+    "acc = (preds == y_test).sum().astype(float) / len(preds)*100\n",
+    "\n",
+    "# Cross Validation\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "\n",
+    "scoring = \"accuracy\"\n",
+    "cv_scores = cross_val_score(model, X_train, y_train, cv=10, scoring = scoring)\n",
+    "cv_scoring_metric_name = 'mean'\n",
+    "cv_scoring_metric = getattr(cv_scores, cv_scoring_metric_name)()\n",
+    "\n",
+    "# Combine train/test split accuracy with CV accuracy\n",
+    "model_metrics = {\n",
+    "    scoring: acc,\n",
+    "    \"cv_scores\": cv_scores,\n",
+    "    \"cv_scoring_metric\": cv_scoring_metric\n",
+    "}\n",
+    "\n",
+    "# Save model performance metric as checkpoint\n",
+    "art = lineapy.save(model_metrics, \"model_metrics\")\n",
+    "\n",
+    "print(model_metrics)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "wDkEdDOJPMgH"
+   },
+   "source": [
+    "### Recap\n",
+    "\n",
+    "So far, we've developed the above code to\n",
+    "\n",
+    "* Read raw data from the data source (and save the raw data as an artifact)\n",
+    "* Done exploratory data analysis\n",
+    "* Performed feature engineering (and saved the engineered feature as an artifact)\n",
+    "* Trained an SVC model (and saved the trained model as an artifact)\n",
+    "* Evaluated the SVC model (and saved the metric as an artifact)\n",
+    "\n",
+    "We saved some artifacts as checkpoints(in the following ordering) to help us create reusable components from the above code we've executed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 190
+    },
+    "id": "T8NhG8SROzfd",
+    "jupyter": {
+     "source_hidden": true
+    },
+    "outputId": "ffd670a3-9c85-4d2b-bfd1-876920a4903c",
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "graph = \"\"\"\n",
+    "graph LR;\n",
+    "RD[raw_data] --> FD[fe_data]\n",
+    "FD --> M[model]\n",
+    "M --> MM[model_metrics]\n",
+    "\"\"\"\n",
+    "\n",
+    "flowchart(graph, 200, 'Artifact Sequence')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "O_eSP0Mornrk",
+    "tags": []
+   },
+   "source": [
+    "## Part 2: Create Reusable Components\n",
+    "\n",
+    "During the model development process, there are many reasons to modify the above code, such as\n",
+    "* Using different data and redoing the end-to-end process to adapt more realistic cases.\n",
+    "* Using different feature sets to improve the model.\n",
+    "* Training different models to find the best model.\n",
+    "* Using different metrics to align with business objectives.\n",
+    "\n",
+    "The conventional wisdom for the user to achieve these goals are\n",
+    "* At the early stage of development, data scientists will make several copies of the notebook and make some changes here and there; they eventually lose track of the notebook.\n",
+    "* At the later stage of the development, data engineers will refactor the code as a standalone module and share the library across the team. The module becomes very rigid and slow-moving if any changes need to happen so that data scientists will go back to the notebook copying path again.\n",
+    "\n",
+    "`LineaPy` provides two methods to reuse the workflow from their notebook. There is no need to duplicate the notebook endlessly but still keep the development process flexible enough. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "9aE9IYTSU7hN"
+   },
+   "source": [
+    "\n",
+    "### 1. Reusable component as a Python function. \n",
+    "\n",
+    "`LineaPy` can generate a parameterized workflow that calculates the list of variables(previously registered as artifacts) as a Python function.\n",
+    "This can be achieved by specifying a list of input variables users want to parameterize and the list of artifacts(checkpoints) they want to recompute to `lineapy.get_function(artifacts, input_parameters)` API.\n",
+    "\n",
+    "Let's say we want to train a stochastic gradient descent(SGD) model and use minimal accuracy as our metric to evaluate the model. \n",
+    "We can achieve this by following code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Hvde5HeVcchY",
+    "outputId": "14b6855d-7458-4b61-ee4e-79eb1e0aec52"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'model_metrics': {'accuracy': 90.87878787878788,\n",
+       "  'cv_scores': array([0.9238806 , 0.92238806, 0.91343284, 0.90447761, 0.91791045,\n",
+       "         0.92238806, 0.91791045, 0.89552239, 0.94029851, 0.9238806 ]),\n",
+       "  'cv_scoring_metric': 0.8955223880597015}}"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# NBVAL_IGNORE_OUTPUT\n",
+    "# Want to use different model and evaluate with different scoring metric.\n",
+    "\n",
+    "calculate_metrics = lineapy.get_function(\n",
+    "  artifacts=[\"model_metrics\"], \n",
+    "  input_parameters=[\"import_module\",\"model_name\",\"scoring\",\"cv_scoring_metric_name\"], \n",
+    ")\n",
+    "\n",
+    "sgd_metrics = calculate_metrics(\n",
+    "    import_module='sklearn.linear_model', \n",
+    "    model_name='SGDClassifier', \n",
+    "    scoring='accuracy', \n",
+    "    cv_scoring_metric_name='min'\n",
+    ")\n",
+    "sgd_metrics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "tRhqG4apwHZ9"
+   },
+   "source": [
+    "### 2. Reusable components as a Python module. \n",
+    "\n",
+    "`LineaPy` can also create a Python module that includes several functions, and each function is responsible for calculating one artifact or common objects of multiple artifacts.\n",
+    "To be more specific, we've saved four artifacts(`raw_data`, `featured_data`, ` model`, `metric`) in our example workflow, and we might want to \n",
+    "1. change the `url`, so we can train the model with different data,\n",
+    "1. change the `import_module` and `model_name` so we can train different models,\n",
+    "1. change the `scoring` and `cv_scoring_metric_name` so we use a different metric to evaluate the model performance.\n",
+    "\n",
+    "These can be achieved by `lineapy.get_module` API as following"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "41sVKDSSb_X8",
+    "outputId": "cba35452-da35-46cd-d723-a87e8ecb1c4b"
+   },
+   "outputs": [],
+   "source": [
+    "helper_module = lineapy.get_module(\n",
+    "  [\"raw_data\",\"fe_data\",\"model\",\"model_metrics\"], \n",
+    "  input_parameters=[\"url\",\"import_module\",\"model_name\",\"scoring\",\"cv_scoring_metric_name\"],   \n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "BDIwxq0PWgIZ"
+   },
+   "source": [
+    "If we investigate objects within the module, we can see some function starts with *get_* and ends with the artifact name like"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "import argparse\n",
+      "import importlib\n",
+      "import importlib.util\n",
+      "from importlib.abc import Loader\n",
+      "\n",
+      "import pandas as pd\n",
+      "from sklearn.decomposition import PCA\n",
+      "from sklearn.ensemble import RandomForestClassifier\n",
+      "from sklearn.linear_model import SGDClassifier\n",
+      "from sklearn.metrics import confusion_matrix, f1_score, precision_score, recall_score\n",
+      "from sklearn.model_selection import cross_val_predict, cross_val_score, train_test_split\n",
+      "from sklearn.naive_bayes import GaussianNB\n",
+      "from sklearn.neighbors import KNeighborsClassifier\n",
+      "from sklearn.preprocessing import LabelEncoder, MinMaxScaler\n",
+      "from sklearn.svm import SVC\n",
+      "\n",
+      "\n",
+      "def get_raw_data(url):\n",
+      "    sdss_df = pd.read_csv(url)\n",
+      "    return sdss_df\n",
+      "\n",
+      "\n",
+      "def get_fe_data(sdss_df):\n",
+      "    sdss_df_fe = sdss_df.drop(\n",
+      "        [\"objid\", \"run\", \"rerun\", \"camcol\", \"field\", \"specobjid\"], axis=1\n",
+      "    )\n",
+      "    le = LabelEncoder()\n",
+      "    y_encoded = le.fit_transform(sdss_df_fe[\"class\"])\n",
+      "    sdss_df_fe[\"class\"] = y_encoded\n",
+      "    pca = PCA(n_components=3)\n",
+      "    ugriz = pca.fit_transform(sdss_df_fe[[\"u\", \"g\", \"r\", \"i\", \"z\"]])\n",
+      "    sdss_df_fe = pd.concat((sdss_df_fe, pd.DataFrame(ugriz)), axis=1)\n",
+      "    sdss_df_fe.rename({0: \"PCA_1\", 1: \"PCA_2\", 2: \"PCA_3\"}, axis=1, inplace=True)\n",
+      "    sdss_df_fe.drop([\"u\", \"g\", \"r\", \"i\", \"z\"], axis=1, inplace=True)\n",
+      "    return sdss_df_fe\n",
+      "\n",
+      "\n",
+      "def get_x_test_x_train_y_test_y_train_for_artifact_model_and_downstream(sdss_df_fe):\n",
+      "    scaler = MinMaxScaler()\n",
+      "    sdss = scaler.fit_transform(sdss_df_fe.drop(\"class\", axis=1))\n",
+      "    X_train, X_test, y_train, y_test = train_test_split(\n",
+      "        sdss, sdss_df_fe[\"class\"], test_size=0.33\n",
+      "    )\n",
+      "    return X_test, X_train, y_test, y_train\n",
+      "\n",
+      "\n",
+      "def get_model(X_train, import_module, model_name, y_train):\n",
+      "    def get_sklearn_model(import_module: str, model_name: str, model_params: dict):\n",
+      "        \"\"\"Returns a scikit-learn model\"\"\"\n",
+      "        model_class = getattr(importlib.import_module(import_module), model_name)\n",
+      "        model = model_class(**model_params)  # Instantiates the model\n",
+      "        return model\n",
+      "\n",
+      "    model_configuration = {}\n",
+      "    model = get_sklearn_model(import_module, model_name, model_configuration)\n",
+      "    model.fit(X_train, y_train)\n",
+      "    return model\n",
+      "\n",
+      "\n",
+      "def get_model_metrics(\n",
+      "    X_test, X_train, cv_scoring_metric_name, model, scoring, y_test, y_train\n",
+      "):\n",
+      "    preds = model.predict(X_test)\n",
+      "    acc = (preds == y_test).sum().astype(float) / len(preds) * 100\n",
+      "    cv_scores = cross_val_score(model, X_train, y_train, cv=10, scoring=scoring)\n",
+      "    cv_scoring_metric = getattr(cv_scores, cv_scoring_metric_name)()\n",
+      "    model_metrics = {\n",
+      "        scoring: acc,\n",
+      "        \"cv_scores\": cv_scores,\n",
+      "        \"cv_scoring_metric\": cv_scoring_metric,\n",
+      "    }\n",
+      "    return model_metrics\n",
+      "\n",
+      "\n",
+      "def run_session_including_raw_data(\n",
+      "    url=\"https://raw.githubusercontent.com/LineaLabs/lineapy/main/examples/use_cases/creating_reusable_components/data/Skyserver_SQL2_27_2018%206_51_39%20PM.csv\",\n",
+      "    import_module=\"sklearn.svm\",\n",
+      "    model_name=\"SVC\",\n",
+      "    scoring=\"accuracy\",\n",
+      "    cv_scoring_metric_name=\"mean\",\n",
+      "):\n",
+      "    # Given multiple artifacts, we need to save each right after\n",
+      "    # its calculation to protect from any irrelevant downstream\n",
+      "    # mutations (e.g., inside other artifact calculations)\n",
+      "    import copy\n",
+      "\n",
+      "    artifacts = dict()\n",
+      "    sdss_df = get_raw_data(url)\n",
+      "    artifacts[\"raw_data\"] = copy.deepcopy(sdss_df)\n",
+      "    sdss_df_fe = get_fe_data(sdss_df)\n",
+      "    artifacts[\"fe_data\"] = copy.deepcopy(sdss_df_fe)\n",
+      "    (\n",
+      "        X_test,\n",
+      "        X_train,\n",
+      "        y_test,\n",
+      "        y_train,\n",
+      "    ) = get_x_test_x_train_y_test_y_train_for_artifact_model_and_downstream(sdss_df_fe)\n",
+      "    model = get_model(X_train, import_module, model_name, y_train)\n",
+      "    artifacts[\"model\"] = copy.deepcopy(model)\n",
+      "    model_metrics = get_model_metrics(\n",
+      "        X_test, X_train, cv_scoring_metric_name, model, scoring, y_test, y_train\n",
+      "    )\n",
+      "    artifacts[\"model_metrics\"] = copy.deepcopy(model_metrics)\n",
+      "    return artifacts\n",
+      "\n",
+      "\n",
+      "def run_all_sessions(\n",
+      "    url=\"https://raw.githubusercontent.com/LineaLabs/lineapy/main/examples/use_cases/creating_reusable_components/data/Skyserver_SQL2_27_2018%206_51_39%20PM.csv\",\n",
+      "    import_module=\"sklearn.svm\",\n",
+      "    model_name=\"SVC\",\n",
+      "    scoring=\"accuracy\",\n",
+      "    cv_scoring_metric_name=\"mean\",\n",
+      "):\n",
+      "    artifacts = dict()\n",
+      "    artifacts.update(\n",
+      "        run_session_including_raw_data(\n",
+      "            url, import_module, model_name, scoring, cv_scoring_metric_name\n",
+      "        )\n",
+      "    )\n",
+      "    return artifacts\n",
+      "\n",
+      "\n",
+      "if __name__ == \"__main__\":\n",
+      "    # Edit this section to customize the behavior of artifacts\n",
+      "    parser = argparse.ArgumentParser()\n",
+      "    parser.add_argument(\n",
+      "        \"--url\",\n",
+      "        type=str,\n",
+      "        default=\"https://raw.githubusercontent.com/LineaLabs/lineapy/main/examples/use_cases/creating_reusable_components/data/Skyserver_SQL2_27_2018%206_51_39%20PM.csv\",\n",
+      "    )\n",
+      "    parser.add_argument(\"--import_module\", type=str, default=\"sklearn.svm\")\n",
+      "    parser.add_argument(\"--model_name\", type=str, default=\"SVC\")\n",
+      "    parser.add_argument(\"--scoring\", type=str, default=\"accuracy\")\n",
+      "    parser.add_argument(\"--cv_scoring_metric_name\", type=str, default=\"mean\")\n",
+      "    args = parser.parse_args()\n",
+      "    artifacts = run_all_sessions(\n",
+      "        url=args.url,\n",
+      "        import_module=args.import_module,\n",
+      "        model_name=args.model_name,\n",
+      "        scoring=args.scoring,\n",
+      "        cv_scoring_metric_name=args.cv_scoring_metric_name,\n",
+      "    )\n",
+      "    print(artifacts)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "helper_module_text = lineapy.get_module_definition(\n",
+    "  [\"raw_data\",\"fe_data\",\"model\",\"model_metrics\"], \n",
+    "  input_parameters=[\"url\",\"import_module\",\"model_name\",\"scoring\",\"cv_scoring_metric_name\"],   \n",
+    ")\n",
+    "print(helper_module_text)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ba5G4JDeVE2A",
+    "tags": []
+   },
+   "source": [
+    "#### What Is Happening Here?\n",
+    "\n",
+    "Dependent on the user-selected list of artifacts, `LineaPy` can divide one computation graph(a representation of your code) into multiple non-overlapping subgraphs. \n",
+    "Based on the user-selected list of input parameters, `LineaPy` can also parametrize the workflow.\n",
+    "\n",
+    "```\n",
+    "lineapy.get_module(\n",
+    "  [\"raw_data\",\"fe_data\",\"model\",\"model_metrics\"], \n",
+    "  input_parameters=[\"url\",\"import_module\",\"model_name\",\"scoring\",\"cv_scoring_metric_name\"],   \n",
+    ")\n",
+    "```\n",
+    "\n",
+    "In this case, we are expecting `get_raw_data()`, `get_fe_data()`, `get_model()`, and `get_model_metrics()` four functions, and each one corresponds to an artifact. \n",
+    "However, we can see one extra function, `get_x_test_x_train_y_test_y_train_for_artifact_model_and_downstream()`, shows up in the module file as well.\n",
+    "The reason is that `X_train` and `y_train` are used in both `get_model()` and `get_model_metrics()`; furthermore, `X_test`, `X_train`, `y_test` and `y_train` are created at the same statement.\n",
+    "Since `LineaPy` break up the code in a non-overlapping way; thus, we are seeing an extra `get_x_test_x_train_y_test_y_train_for_artifact_model_and_downstream()` function shows up here.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 708
+    },
+    "id": "n_K8oUceImbD",
+    "jupyter": {
+     "source_hidden": true
+    },
+    "outputId": "07e098c2-6a2f-41b6-fb73-80ee90186f36",
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x960 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "graph = \"\"\"\n",
+    "graph\n",
+    "\n",
+    "subgraph input\n",
+    "url[/url/]\n",
+    "im[/input_module/]\n",
+    "mn[/module_name/]\n",
+    "s[/scoring/]\n",
+    "csmn[/cv_scoring_metric_name/]\n",
+    "end \n",
+    "\n",
+    "subgraph workflow\n",
+    "GRD[\"get_raw_data()\"] \n",
+    "GFD[\"get_fe_data()\"]\n",
+    "GXXYY[\"get_x_test_x_train_y_test_y_train_for_artifact_model_and_downstream()\"]\n",
+    "GM[\"get_model()\"]\n",
+    "GMM[\"get_model_metrics()\"]\n",
+    "xtr[/X_train/]\n",
+    "ytr[/y_train/]\n",
+    "xte[/X_test/]\n",
+    "yte[/y_test/]\n",
+    "end\n",
+    "\n",
+    "subgraph output\n",
+    "rd[/raw_daga/]\n",
+    "fd[/fe_data/]\n",
+    "m[/model/]\n",
+    "mm[/model_metrics/]\n",
+    "end\n",
+    "\n",
+    "url --> GRD\n",
+    "GRD --> rd \n",
+    "rd --> GFD\n",
+    "GFD --> fd\n",
+    "fd --> GXXYY\n",
+    "GXXYY --> xtr & ytr & xte & yte\n",
+    "im & mn & xtr & ytr --> GM\n",
+    "GM --> m\n",
+    "xtr & ytr & xte & yte & csmn & s & m --> GMM\n",
+    "GMM --> mm\n",
+    "\"\"\"\n",
+    "\n",
+    "flowchart(graph, 240, 'Parameterized Workflow')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Reuse Functions within the Module\n",
+    "\n",
+    "After investigating all the function definitions within the module, we can reuse these functions individually to achieve more than the parameterization described in the previous example.\n",
+    "For instance, we want to use `keras` to train a three-layer dense neural network model with different dropout rates on the same training data set and features. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Reuse Functions to Load Data\n",
+    "\n",
+    "Here, we can reuse `get_raw_data()`, `get_fe_data()`, and `get_x_test_x_train_y_test_y_train_for_artifact_model_and_downstream()` to prepare the same training and validation data set as before."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "0LYr9cO6bn1C",
+    "outputId": "0f15835a-81c1-46e2-d10b-79bf403fdff6"
+   },
+   "outputs": [],
+   "source": [
+    "# Load raw data from artifact store\n",
+    "url = \"https://raw.githubusercontent.com/LineaLabs/lineapy/main/examples/use_cases/creating_reusable_components/data/Skyserver_SQL2_27_2018%206_51_39%20PM.csv\" \n",
+    "new_raw_data = helper_module.get_raw_data(url)\n",
+    "\n",
+    "# Do some tweak on the feature engineering \n",
+    "new_fe_data = helper_module.get_fe_data(new_raw_data)\n",
+    "new_X_test, new_X_train, new_y_test, new_y_train = helper_module.get_x_test_x_train_y_test_y_train_for_artifact_model_and_downstream(new_fe_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "xIMDhLSncE4l"
+   },
+   "source": [
+    "#### Train a Keras Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "0LYr9cO6bn1C",
+    "outputId": "0f15835a-81c1-46e2-d10b-79bf403fdff6"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2023-01-30 12:10:21.621262: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
+      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
+      "2023-01-30 12:10:21.740631: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
+      "2023-01-30 12:10:21.740648: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
+      "2023-01-30 12:10:22.332167: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n",
+      "2023-01-30 12:10:22.332234: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n",
+      "2023-01-30 12:10:22.332241: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n",
+      "2023-01-30 12:10:24.521028: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n",
+      "2023-01-30 12:10:24.521088: W tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:265] failed call to cuInit: UNKNOWN ERROR (303)\n",
+      "2023-01-30 12:10:24.521147: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (mjl001): /proc/driver/nvidia/version does not exist\n",
+      "2023-01-30 12:10:24.521746: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
+      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"sequential\"\n",
+      "_________________________________________________________________\n",
+      " Layer (type)                Output Shape              Param #   \n",
+      "=================================================================\n",
+      " flatten (Flatten)           (None, 9)                 0         \n",
+      "                                                                 \n",
+      " dense (Dense)               (None, 256)               2560      \n",
+      "                                                                 \n",
+      " dense_1 (Dense)             (None, 128)               32896     \n",
+      "                                                                 \n",
+      " dropout (Dropout)           (None, 128)               0         \n",
+      "                                                                 \n",
+      " dense_2 (Dense)             (None, 64)                8256      \n",
+      "                                                                 \n",
+      " dropout_1 (Dropout)         (None, 64)                0         \n",
+      "                                                                 \n",
+      " dense_3 (Dense)             (None, 32)                2080      \n",
+      "                                                                 \n",
+      " dropout_2 (Dropout)         (None, 32)                0         \n",
+      "                                                                 \n",
+      " dense_4 (Dense)             (None, 3)                 99        \n",
+      "                                                                 \n",
+      "=================================================================\n",
+      "Total params: 45,891\n",
+      "Trainable params: 45,891\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n",
+      "None\n",
+      "Keras weights file (<HDF5 file \"variables.h5\" (mode r+)>) saving:\n",
+      "...layers\n",
+      "......dense\n",
+      ".........vars\n",
+      "............0\n",
+      "............1\n",
+      "......dense_1\n",
+      ".........vars\n",
+      "............0\n",
+      "............1\n",
+      "......dense_2\n",
+      ".........vars\n",
+      "............0\n",
+      "............1\n",
+      "......dense_3\n",
+      ".........vars\n",
+      "............0\n",
+      "............1\n",
+      "......dense_4\n",
+      ".........vars\n",
+      "............0\n",
+      "............1\n",
+      "......dropout\n",
+      ".........vars\n",
+      "......dropout_1\n",
+      ".........vars\n",
+      "......dropout_2\n",
+      ".........vars\n",
+      "......flatten\n",
+      ".........vars\n",
+      "...metrics\n",
+      "......mean\n",
+      ".........vars\n",
+      "............0\n",
+      "............1\n",
+      "......mean_metric_wrapper\n",
+      ".........vars\n",
+      "............0\n",
+      "............1\n",
+      "...optimizer\n",
+      "......vars\n",
+      ".........0\n",
+      ".........1\n",
+      ".........10\n",
+      ".........2\n",
+      ".........3\n",
+      ".........4\n",
+      ".........5\n",
+      ".........6\n",
+      ".........7\n",
+      ".........8\n",
+      ".........9\n",
+      "...vars\n",
+      "Keras model archive saving:\n",
+      "File Name                                             Modified             Size\n",
+      "config.json                                    2023-01-30 12:10:29         3655\n",
+      "metadata.json                                  2023-01-30 12:10:29           64\n",
+      "variables.h5                                   2023-01-30 12:10:30       403480\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "LineaArtifact(name='nn_model', _version=8)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# NBVAL_SKIP\n",
+    "import tensorflow as tf\n",
+    "from tensorflow import keras\n",
+    "import lineapy\n",
+    "\n",
+    "# Here are we are not using cross validation but using a hold out set for validation during the training\n",
+    "XTrain, XVal, YTrain, YVal = train_test_split(new_X_train, new_y_train, test_size = 0.1, random_state=101)\n",
+    "\n",
+    "# We might want to tune the dropout rate between different layer \n",
+    "dropout1 = 0.25\n",
+    "dropout2 = 0.25\n",
+    "dropout3 = 0.5\n",
+    "\n",
+    "# Construct the model\n",
+    "kmodel = keras.models.Sequential([\n",
+    "    keras.layers.Flatten(),\n",
+    "    keras.layers.Dense(256, activation='relu'),\n",
+    "    keras.layers.Dense(128, activation='relu'),\n",
+    "    keras.layers.Dropout(dropout1),\n",
+    "    keras.layers.Dense(64, activation='relu'),\n",
+    "    keras.layers.Dropout(dropout2),\n",
+    "    keras.layers.Dense(32, activation='relu'),\n",
+    "    keras.layers.Dropout(dropout3),\n",
+    "    keras.layers.Dense(3, activation='softmax')\n",
+    "])\n",
+    "# Set opimizer\n",
+    "optimizer = tf.keras.optimizers.RMSprop()\n",
+    "# Compile the model\n",
+    "kmodel.compile(optimizer=optimizer , loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
+    "# Set training parameters\n",
+    "learning_rate_reduction = keras.callbacks.ReduceLROnPlateau(\n",
+    "    monitor='accuracy', \n",
+    "    patience=3, \n",
+    "    verbose=1, \n",
+    "    factor=0.5, \n",
+    "    min_lr=0.00001)\n",
+    "# Train the model\n",
+    "history = kmodel.fit(\n",
+    "    XTrain, YTrain,\n",
+    "    batch_size=86,\n",
+    "    epochs = 30,\n",
+    "    validation_data = (XVal,YVal),\n",
+    "    verbose=0,\n",
+    "    callbacks=[learning_rate_reduction]\n",
+    ")\n",
+    "\n",
+    "print(kmodel.summary())\n",
+    "\n",
+    "# Save trained model as artifact\n",
+    "lineapy.save(kmodel, 'nn_model')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ENOeYjJSixqq"
+   },
+   "source": [
+    "#### Evaluate the model performance\n",
+    "\n",
+    "Now we can evaluate our neural network model performance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "104/104 [==============================] - 0s 696us/step\n",
+      "104/104 [==============================] - 0s 835us/step - loss: 0.0923 - accuracy: 0.9755\n",
+      "[0.09232450276613235, 0.975454568862915]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# NBVAL_SKIP\n",
+    "\n",
+    "results = kmodel.predict(X_test)\n",
+    "evaluation_result = kmodel.evaluate(X_test, y_test)\n",
+    "\n",
+    "# Save metric as an artifact\n",
+    "art = lineapy.save(evaluation_result, 'nn_model_evaluation')\n",
+    "print(evaluation_result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "c1ec976a-51d8-4bb2-b93b-bdf30981f277",
+    "_uuid": "87c2b7276cbe466417b8f696c93dc7055a3454ae",
+    "id": "3trrVRCRQFMw"
+   },
+   "source": [
+    "## Recap\n",
+    "\n",
+    "In this tutorial, we've \n",
+    "1. used a machine learning model developing example to demonstrate how to save artifacts as checkpoints and create reusable functions and modules to reuse the workflow,\n",
+    "1. explained how these reusable components are created\n",
+    "1. demonstrated how to use these reusable components in two different ways.\n",
+    "\n",
+    "These reusable components can help individual users with\n",
+    "1. organizing their development and experiments\n",
+    "\n",
+    "These reusable components can help organizations with\n",
+    "1. sharing work between teammates\n",
+    "1. deploying standardized components within the organization\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## What Next"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"admonition info\" style=\"background:#F0FAFA\">\n",
+    "    <p class=\"admonition-title\"><b>Info</b></p>\n",
+    "    <p>\n",
+    "        If you want to learn more about LineaPy's pipeline support, check out the project <a href=\"https://docs.lineapy.org/latest/guides/usage/pipeline-building/\">documentation</a>.\n",
+    "    </p>\n",
+    "</div>"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "collapsed_sections": [
+    "c0cVXS1nbaFc"
+   ],
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "cacea137424fd3104c427a06c692d13a169698dd1bd116af82859e9688265e62"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}