Advanced MLOps: End-to-End Machine Learning Pipeline with Random Forest Classifier

Welcome to the Advanced MLOps repository. This project demonstrates the end-to-end workflow for building a Machine Learning (ML) pipeline that includes data preprocessing, model training, hyperparameter tuning, and model deployment using MLOps best practices. The project focuses on automating and optimizing every phase of the machine learning lifecycle using Random Forest Classifier.

Project Overview

In this project, we explore how MLOps principles can be applied to streamline the entire ML process—from raw data to a deployed and monitored machine learning model. Key features include:

• Data Preprocessing: Automation of data wrangling and feature engineering steps.
• Model Training: Training a Random Forest model and optimizing hyperparameters.
• Model Evaluation: Robust evaluation metrics and model interpretability.
• Deployment: CI/CD pipeline to deploy the model on a cloud platform.
• Monitoring: Real-time model monitoring with logging and performance alerts.
• Version Control: Full version control of data, model, and pipeline using Git & DVC (Data Version Control).

Key Features

1. Automated Machine Learning Pipeline

• Data Ingestion: Automated scripts for importing and cleaning data.
• Data Preprocessing: Feature scaling, encoding, and missing value imputation.
• Model Training & Hyperparameter Tuning: Using Random Forest Classifier with an automated hyperparameter search (GridSearchCV, RandomSearchCV, or Bayesian Optimization).
• Model Validation: Cross-validation and performance metrics (Accuracy, AUC, F1 Score).

2. MLOps Workflow

• Continuous Integration/Continuous Deployment (CI/CD): Use of tools like Jenkins or GitHub Actions to automate testing and deployment.
• Infrastructure as Code (IaC): Automated infrastructure setup using Terraform or similar tools.
• Containerization: Docker to encapsulate the model and its dependencies.
• Kubernetes: Orchestration of containerized services for scalable deployment.

3. Version Control

• Data Versioning: Implemented with DVC (Data Version Control) to track dataset changes over time.
• Model Versioning: Each version of the model is tracked using a model registry (e.g., MLflow, DVC, or Git LFS).
• Pipeline Versioning: End-to-end pipeline tracking for reproducibility.

4. Monitoring and Alerts

• Real-time Monitoring: Using Prometheus and Grafana for real-time metrics like latency, prediction accuracy, and throughput.
• Model Drift Detection: Periodic checks to detect performance degradation.
• Logging: Centralized logging using ELK Stack (Elasticsearch, Logstash, Kibana) for debugging and monitoring.

5. Model Deployment

• REST API: Deploy model as a REST API using FastAPI or Flask.
• Cloud Deployment: Use of Azure, AWS, or Google Cloud Platform for scalable model deployment.
• CI/CD Pipeline: Automated deployment pipeline with tools like Jenkins, GitHub Actions, or Azure Pipelines.

6. Reproducibility

• MLflow/DVC: Ensure reproducibility with full pipeline tracking and data version control.
• Docker: Containerized environment for consistent model execution across different systems.

Project Structure

.
├── data/                # Raw and processed data
├── notebooks/           # Jupyter Notebooks for exploratory analysis
├── src/
│   ├── data_ingestion.py # Scripts for data collection and preprocessing
│   ├── model_training.py # Model training and evaluation scripts
│   ├── hyperparameter_tuning.py # Scripts for tuning Random Forest Classifier
│   ├── deployment.py     # Scripts for deploying the model
│   └── monitoring.py     # Monitoring and logging setup
├── Dockerfile            # Dockerfile for containerizing the model
├── requirements.txt      # Python dependencies
├── README.md             # Project documentation
└── .github/              # CI/CD workflow configurations

How to Run the Project

1. Clone the Repository

git clone https://github.com/AyushAI/MLOPS-TITANIC-SURVIVAL.git
cd advanced-mlops-pipeline

2. Setup Virtual Environment

python3 -m venv env
source env/bin/activate

4. Data Ingestion and Preprocessing

Copy code
python src/data_ingestion.py

6. Model Training and Hyperparameter Tuning

Copy code
python src/model_training.py
python src/hyperparameter_tuning.py

7. Deployment

Copy code
python src/deployment.py

8. Monitoring

Copy code
python src/monitoring.py

CI/CD Pipeline

The CI/CD pipeline automates testing, validation, and deployment using GitHub Actions/Jenkins. Each commit triggers the following steps:

• Unit Testing: Tests for data preprocessing and model scripts.
• Build Docker Image: Containerization of the model.
• Deploy to Cloud: Automated deployment to the cloud infrastructure.

Future Improvements

• Advanced Hyperparameter Tuning: Integration of Bayesian Optimization.
• Advanced Model Monitoring: Integration of drift detection using statistical tests.
• Experiment Tracking: Integrating with MLflow or Weights & Biases for better experiment tracking.

Contributing

Feel free to submit a pull request. Please make sure your code adheres to the following guidelines:

• Code should be well-documented and use type annotations.
• Follow PEP8 guidelines for Python code.
• Ensure that any changes include corresponding tests.

License

This project is licensed under the MIT License - see the LICENSE file for details.