Enterprise ML Platform

Personal Project: A comprehensive end-to-end MLOps platform for developing, deploying, and monitoring machine learning models at scale. This enterprise-grade solution integrates modern ML engineering practices with robust DevOps principles to streamline the entire ML lifecycle.

📋 Table of Contents

• Architecture Overview
• Key Features
• Components
• Getting Started
• Development Workflow
• Model Training and Deployment
• Monitoring and Observability
• CI/CD Integration
• Contributing
• License

🏗️ Architecture Overview

The Enterprise ML Platform is built on a modular architecture that separates concerns while maintaining seamless integration between components:

Enterprise ML Platform
├── Model Training ─────┐
│   • PyTorch           │
│   • TensorFlow        │    ┌─── Model Serving
│   • XGBoost/LightGBM  │────┤    • Flask API
│   • LLM (BERT/GPT)    │    │    • Elasticsearch
│                       │    │    • Azure Functions
├── Data Pipeline ──────┤    │
│   • Spark/Databricks  │    │
│   • Azure Data Factory├────┤
│   • Data Quality      │    │    ┌─── Monitoring
│                       │    │    │    • Prometheus
├── Infrastructure ─────┤    │    │    • Grafana
│   • Azure ML          ├────┼────┤    • Azure Monitor
│   • Kubernetes        │    │    │
│   • Docker            │    │    │
│                       │    │    │
└── DevOps ─────────────┘    └────┘
    • Jenkins
    • SonarQube
    • Git

This platform enables:

• Robust ETL pipelines with data quality validation
• Flexible model training with multiple ML frameworks
• Scalable model serving via REST APIs
• Comprehensive monitoring and observability
• Full CI/CD integration for MLOps

✨ Key Features

• End-to-End ML Lifecycle Management: From data ingestion to model deployment and monitoring
• Scalable Architecture: Cloud-native design using Kubernetes for horizontal scaling
• Model Flexibility: Support for various ML frameworks (XGBoost, LightGBM, PyTorch, BERT)
• Robust Data Processing: ETL pipeline with data quality checks
• Real-time Monitoring: Comprehensive metrics for model performance and system health
• DevOps Integration: CI/CD pipelines for automated testing and deployment
• Cloud Ready: Designed for Azure with support for Azure ML, AKS, and other services

🧩 Components

Data Pipeline

• data_pipeline/spark/etl_pipeline.py: Spark-based ETL pipeline for data processing
• data_pipeline/quality_checks/data_validator.py: Framework for validating data quality

Model Training

• model_training/sklearn/xgboost.py: Implementation of XGBoost and LightGBM models
• model_training/llm/bert_classifier.py: Text classification using BERT

Model Serving

• model_serving/flask/app.py: Flask API for serving ML models
• infrastructure/docker/Dockerfile: Docker configuration for containerizing the API

Infrastructure

• infrastructure/kubernetes/ml-api-deployment.yaml: Kubernetes deployment configuration
• Azure ML integration (templates and configurations)

Monitoring

• monitoring/prometheus/prometheus-config.yaml: Prometheus configuration
• monitoring/grafana/ml-api-dashboard.json: Grafana dashboard for monitoring

DevOps

• Jenkins pipeline configurations
• Integration with SonarQube for code quality

🚀 Getting Started

Prerequisites

• Python 3.7+
• Docker and Docker Compose
• Kubernetes cluster (or minikube for local development)
• Azure account (for cloud deployment)

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/enterprise-ml-platform.git
   cd enterprise-ml-platform

2. Create a virtual environment and install dependencies:

   python -m venv venv
   source venv/bin/activate  # On Windows, use: venv\Scripts\activate
   pip install -r requirements.txt

3. Build the Docker image:

   docker build -t ml-api:latest -f infrastructure/docker/Dockerfile .

Local Development Setup

1. Start the Flask API locally:

   python model_serving/flask/app.py

2. For local Kubernetes deployment:

   kubectl apply -f infrastructure/kubernetes/ml-api-deployment.yaml

3. Set up monitoring:

   kubectl apply -f monitoring/prometheus/prometheus-config.yaml
   # Import the Grafana dashboard JSON file manually through the Grafana UI

🔄 Development Workflow

The recommended workflow for developing and extending the platform:

1. Data Preparation: Use the ETL pipeline to process and validate your dataset

   python data_pipeline/spark/etl_pipeline.py

2. Model Training: Train models using the provided framework

   python model_training/sklearn/xgboost.py

3. Model Evaluation: Evaluate model performance using standard metrics

4. Model Deployment: Deploy the model as a REST API

   # Update model path in Flask app
   docker build -t ml-api:latest -f infrastructure/docker/Dockerfile .
   kubectl apply -f infrastructure/kubernetes/ml-api-deployment.yaml

5. Monitoring: Track model performance and system health through Grafana dashboards

📦 Model Training and Deployment

Training a New Model

Example of training an XGBoost model:

from model_training.sklearn.xgboost import SklearnModelTrainer

trainer = SklearnModelTrainer(model_type="xgboost")
model_path, metrics = trainer.run_training_pipeline(
    data_path="./data/processed/customer_data.parquet",
    target_column="churn"
)

print(f"Model saved to {model_path}")
print(f"Model metrics: {metrics}")

Making Predictions

Once deployed, you can make predictions using the API:

curl -X POST \
  http://localhost:5000/predict/tabular \
  -H 'Content-Type: application/json' \
  -d '{
    "feature1": 0.5,
    "feature2": 1.0,
    "feature3": "category_a"
  }'

For text classification:

curl -X POST \
  http://localhost:5000/predict/text \
  -H 'Content-Type: application/json' \
  -d '{
    "text": "This is an example text for classification."
  }'

🔍 Monitoring and Observability

The platform includes:

• Prometheus metrics: System and application performance
• Custom ML metrics: Model predictions, drift detection
• Grafana dashboard: Real-time visualization
• Logging: Comprehensive logging for debugging and auditing

Access the dashboards:

• Prometheus: http://localhost:9090
• Grafana: http://localhost:3000

🔄 CI/CD Integration

The platform supports integration with Jenkins for continuous integration and deployment:

1. Automated testing of data pipelines
2. Model training and validation
3. Container building and testing
4. Deployment to staging and production environments

🚢 Production Deployment

For production deployment on Azure:

1. Set up Azure resources (AKS, Azure ML)
2. Configure Azure credentials
3. Deploy using Kubernetes manifests
4. Set up monitoring and alerts

🔮 Future Enhancements

Planned future improvements:

• Model A/B testing framework
• Automated hyperparameter optimization
• Feature store integration
• Drift detection and automated retraining
• Enhanced security features

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📝 License

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

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Contact

If you have any questions or feedback, please open an issue or contact [email protected].