ML Automation Pipeline

A comprehensive machine learning automation pipeline for homeowner loss history prediction, featuring real-time monitoring, data quality checks, model explainability, and A/B testing.

Overview

This project implements a production-ready ML pipeline with the following key features:

• Data Ingestion & Preprocessing: Automated data ingestion from S3, preprocessing, and schema validation
• Data Quality Monitoring: Continuous monitoring of data quality with anomaly detection
• Model Explainability: SHAP values and feature importance tracking
• A/B Testing: Robust A/B testing framework for model promotion
• Real-time Monitoring: WebSocket-based real-time dashboard updates
• Automated Retraining: Drift detection and automated model retraining
• Human-in-the-Loop: Manual override capabilities for critical decisions

Architecture

Components

1. Airflow DAGs: Orchestrate the entire ML pipeline
2. Data Quality Monitor: Tracks data quality metrics and detects anomalies
3. Model Explainability Tracker: Monitors model interpretability
4. A/B Testing Pipeline: Manages model comparison and promotion
5. WebSocket Server: Provides real-time updates to the dashboard
6. MLflow Integration: Tracks experiments and model metrics

AWS Infrastructure

1. Amazon MWAA: Managed Apache Airflow service

   • DAGs stored in S3
   • Automatic high availability
   • IAM integration

2. SageMaker Model Registry: Model versioning and deployment

   • Automated model promotion
   • Version control
   • Secure deployment hooks

3. AWS Amplify: Dashboard hosting

   • Automatic builds on main branch
   • HTTPS and custom domain
   • Environment variable management

4. API Gateway + Lambda: Real-time updates

   • WebSocket API
   • Serverless architecture
   • Auto-scaling

5. AWS Secrets Manager: Secure configuration

   • Centralized secrets storage
   • IAM-based access control
   • Encrypted at rest

6. CloudWatch + SNS: Monitoring and alerts

   • Centralized logging
   • Custom metrics
   • Slack integration

Setup

Prerequisites

• Python 3.12+
• Node.js 18+
• AWS CLI configured
• GitHub account
• AWS account with appropriate permissions

Local Development

1. Clone the repository:

   git clone https://github.com/yourusername/ml_automation.git
   cd ml_automation

2. Create a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Set up environment variables:

   cp .env.template .env
   # Edit .env with your configuration

AWS Deployment

1. Infrastructure Setup:

   # Install AWS CDK
   npm install -g aws-cdk
   
   # Deploy infrastructure
   cd infrastructure
   cdk deploy

2. MWAA Environment:

   • Create S3 bucket for DAGs
   • Configure MWAA environment
   • Set up IAM roles

3. Model Registry:

   • Configure SageMaker Model Registry
   • Set up model promotion workflow
   • Configure deployment hooks

4. Dashboard Deployment:

   • Connect GitHub repository to Amplify
   • Configure build settings
   • Set up environment variables

5. WebSocket API:

   • Deploy API Gateway WebSocket API
   • Configure Lambda functions
   • Set up connections

Development

Project Structure

ml_automation/
├── dags/                    # Airflow DAGs
│   ├── tasks/              # Task implementations
│   └── utils/              # Utility functions
├── loss-history-dashboard/ # React dashboard
├── infrastructure/         # AWS CDK code
├── tests/                  # Test files
├── requirements.txt        # Python dependencies
└── .env.template          # Environment template

CI/CD Pipeline

1. GitHub Actions Workflow:

   • Run tests on PR
   • Deploy infrastructure
   • Update MWAA environment
   • Deploy dashboard

2. Infrastructure as Code:

   • AWS CDK for infrastructure
   • Automated deployments
   • Environment management

3. Monitoring:

   • CloudWatch metrics
   • SNS notifications
   • Slack integration

Testing

Run the test suite:

# Backend tests
pytest tests/

# Frontend tests
cd loss-history-dashboard
npm test

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

License

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