# Machine Learning Platform Boilerplate

## Overview
Model training, data pipelines, model deployment, MLOps, AI/ML experimentation

## Tech Stack
- **Backend**: Python, FastAPI, Flask
- **Frontend**: React, TypeScript, Jupyter
- **Database**: PostgreSQL, MongoDB
- **ML/AI**: TensorFlow, PyTorch, Scikit-learn
- **MLOps**: MLflow, Kubeflow, DVC
- **Cloud**: AWS SageMaker, Google AI Platform

## Specialized Agents
- `ml-model-engineer` - Machine learning model development
- `data-pipeline-architect` - ML data processing workflows
- `mlops-deployment-specialist` - Model deployment and monitoring
- `feature-engineering-expert` - Data transformation and feature creation

## Key Features
- Model training and experimentation
- Data preprocessing pipelines
- Model versioning and registry
- Automated model deployment
- Performance monitoring
- A/B testing for models

## ML Workflows
- Data ingestion and validation
- Feature engineering and selection
- Model training and hyperparameter tuning
- Model evaluation and validation
- Deployment and serving
- Monitoring and retraining

## Architecture Patterns
- MLOps pipeline automation
- Microservices model serving
- Event-driven ML workflows
- Distributed model training

## Implementation Requirements
- Scalable model training infrastructure
- Real-time and batch prediction APIs
- Model versioning and rollback
- Data drift detection
- Performance monitoring
- Automated retraining pipelines