Complete Machine Learning Model Deployment Pipeline on AWS SageMaker 🌟

This repository provides an end-to-end pipeline for deploying machine learning models on AWS SageMaker, from data ingestion to model inferencing.

For a step-by-step understanding, refer to my Medium article Deploying Machine Learning Models on Amazon SageMaker

What’s Covered in This Pipeline? 🚀

1. Data Ingestion 📥 Upload dataset to Amazon S3 for training and testing.

2. Model Training 🔧 Train the model using SageMaker with algorithms like RandomForestClassifier. Training is done on cost-effective Spot Instances.

3. Model Deployment 🚀 Deploy the trained model for real-time inference via SageMaker endpoints.

4. Model Inferencing 🔍 Perform inference on new data by sending requests to the endpoint.

Getting Started 🚀 Clone this repository:

git clone https://github.com/yourusername/your-repository.git

Set up AWS CLI: 👉 Getting Started with AWS CLI

Project Structure
your-repository/
│
├── config/                
│   └── config.yaml        # Configuration settings for the project
│
├── artifacts/             # Folder for raw and processed data
│   ├── dataset/
│   │   ├── complete_dataset.csv
│   │   ├── train-V-1.csv
│   │   └── test-V-1.csv
│
├── src/                   # Source code for model training and deployment
│   └── MobilePriceClassification/
│       ├── components/     # Scripts for different model components
│       │   ├── data_ingestion.py   # Data ingestion logic
│       │   ├── model_trainer.py    # Model training logic
│       │   ├── script.py           # Main training script
│       │   ├── model_deploy.py     # Model deployment logic
│       │   └── model_inference.py  # Model inference logic
│       │
│       ├── config/          # Configuration files for the components
│       │   └── configuration.py
│       │
│       ├── constants/       # Constant variables and enums
│       │   └── __init__.py
│       │
│       ├── entity/          # Entity files (such as data schema)
│       │   └── __init__.py
│       │
│       ├── logging/         # Logging utilities
│       │   └── __init__.py
│       │
│       ├── pipeline/        # Pipeline for model stages
│       │   ├── stage_1_data_ingestion_pipeline.py  # Data ingestion pipeline
│       │   ├── stage_2_model_training.py           # Model training pipeline
│       │   ├── stage_3_model_deployment_pipeline.py# Model deployment pipeline
│       │   └── stage_4_model_inferencing_pipeline.py # Model inference pipeline
│       │
│       └── utils/           # Utility functions
│           └── __init__.py
│
├── logs/                  # Logs for tracking model activities
│   └── continuos_logs.log # Logs file
│
├── main.py                # Main script to run the project
├── .env                   # Environment variables for the project
├── requirements.txt       # Dependencies for the project
├── README.md              # Project documentation
└── .gitignore             # Git ignore file