- Add API server with WebSocket support and REST endpoints
- Implement authentication system with API keys and permissions
- Add task queue system with Redis backend and error handling
- Include storage layer with database migrations and schemas
- Add comprehensive logging, metrics, and telemetry
- Implement security middleware and network utilities
- Add experiment management and container orchestration
- Include configuration management with smart defaults
- Add comprehensive README with architecture overview and quick start guide
- Set up Go module with production-ready dependencies
- Configure build system with Makefile for development and production builds
- Add Docker Compose for local development environment
- Include project configuration files (linting, Python, etc.)
This establishes the foundation for a production-ready ML experiment platform
with task queuing, monitoring, and modern CLI/API interface.
