Mlops_model_control_plane_server

This project implements a complete MLOps workflow with a Model Control Plane (MCP) server for model management and deployment.

Features

• Data processing pipeline
• Model training pipeline
• Model registry for versioning and lifecycle management
• FastAPI-based Model Control Plane (MCP) server
• Monitoring with Prometheus and Grafana
• Alerting system
• Dockerized deployment

Project Structure

• config/: Configuration files
• data/: Data storage and processing utilities
• models/: Model implementation and training logic
• pipelines/: Pipeline implementations
• mcp_server/: MCP server implementation
• monitoring/: Metrics and alerts
• utils/: Utility functions
• tests/: Unit and integration tests

Getting Started

Prerequisites

• Python 3.8+
• Docker and Docker Compose (for containerized deployment)

Installation

1. Clone the repository
2. Install dependencies:

   pip install -r requirements.txt
   

Configuration

Edit the config/config.yaml file to customize settings for your environment.

Running the Application

Process Data

python main.py --action process_data --data your_data.csv

Train a Model

python main.py --action train --data processed_data.csv

Run Inference

python main.py --action inference --data new_data.csv --model model_name

Start the MCP Server

python main.py --action serve

Docker Deployment

Build and run the containerized application:

docker-compose up -d

This will start the MCP server, Prometheus, and Grafana.

API Endpoints

• GET /health: Health check endpoint
• GET /models: List all models in the registry
• GET /models/{model_name}: Get information about a specific model
• POST /predict: Make predictions using a model
• PUT /models/{model_name}/{version}/status: Update the status of a model

Monitoring

Access Prometheus metrics at http://localhost:9091 Access Grafana dashboards at http://localhost:3000

Testing

Run tests with pytest:

pytest tests/

License

MIT

Extending the Project

CI/CD Pipeline with GitHub Actions

Future Enhancements:

1. Feature Store: Add a feature store to manage and reuse features across different models.
2. A/B Testing: Implement an A/B testing framework for comparing model versions.
3. AutoML: Add AutoML capabilities for automated model selection and hyperparameter tuning.
4. Security: Implement authentication and authorization for the MCP API.
5. Model Explainability: Add model explainability tools to understand model predictions.
6. Drift Detection: Implement data and model drift detection to alert when data patterns change.
7. Kubernetes Deployment: Add Kubernetes manifests for scalable deployment.