MLOps Project for Titanic Dataset using ZenML and ML Flow

This repository contains the implementation of Titanic Survival Prediction using Scikit-learn. But the Catch here is I have written Production Level Code, which can be made live in your local server and see its stats using ZenML and MLFlow.

Requirements:

• Python 3.x
• Linux or Mac Environment (In windows, use WSL. In my case, I have used Linux to develop this)
• Decent specs to run python scripts, as the ML model is just SVM

Training Pipeline

This script, training_pipeline.py, is a ZenML pipeline that orchestrates the process of training a machine learning model. It includes the following steps:

1. Data Ingestion: The run function from steps.ingest_data module is used to ingest data from a given data path.

2. Data Cleaning: The clean_data function from steps.clean_data module is used to clean the ingested data.

3. Data Splitting: The split_data function from steps.split_data module is used to split the cleaned data into features (X) and target (y).

4. Train and Test Split: The train_and_test_split function from steps.train_and_test_split module is used to split the data into training and testing sets.

5. Model Training: The train_model function from steps.model_train module is used to train the model using the training data.

6. Model Evaluation: The model_eval function from steps.model_eval module is used to evaluate the trained model using the testing data.

Usage

This pipeline is decorated with the @pipeline decorator from ZenML, and takes a single argument: data_path, which is the path to the data to be ingested.

@pipeline
def training_pipeline(data_path: str):
    ...

To run this pipeline, you would typically import it in another script and call it with the path to your data:

from training_pipeline import training_pipeline
training_pipeline('path/to/your/data.csv')

Important Commands

zenml up - To turn up the server
zenml down - To turn down the server of zenml
zenml disconnect - to disconnect zenml server
zenml init - To initialize the zenml folder
zenml stack describe- To see the stack description
zenml stack list - Lists down the stack names along with Stack ID and which stack is active

The project can only be executed with a ZenML stack that has an MLflow experiment tracker and model deployer as a component. Configuring a new stack with the two components are as follows:

zenml integration install mlflow -y
zenml experiment-tracker register mlflow_tracker --flavor=mlflow
zenml model-deployer register mlflow --flavor=mlflow
zenml stack register mlflow_stack -a default -o default -d mlflow -e mlflow_tracker --set
python run_deployment.py --config deploy - To Deploy the Model
python run_deployment.py --config predict - To Predict the results from the Model