- Add development and production configuration templates
- Include Docker build files for containerized deployment
- Add Nginx configuration with SSL/TLS setup
- Include environment configuration examples
- Add SSL certificate setup and management
- Configure application schemas and validation
- Support for both local and production deployment scenarios
Provides flexible deployment options from development to production
with proper security, monitoring, and configuration management.
- Add modern CLI interface built with Zig for performance
- Include TUI (Terminal User Interface) with bubbletea-like features
- Implement ML experiment commands (run, status, manage)
- Add configuration management and validation
- Include shell completion scripts for bash and zsh
- Add comprehensive CLI testing framework
- Support for multiple ML frameworks and project types
CLI provides fast, efficient interface for ML experiment management
with modern terminal UI and comprehensive feature set.
- 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.
