2.1 KiB
2.1 KiB
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| Fetch ML Documentation |
Fetch ML - Secure Machine Learning Platform
A secure, containerized platform for running machine learning experiments with role-based access control and comprehensive audit trails.
Quick Start
New to the project? Start here!
# Clone the repository
git clone https://github.com/your-username/fetch_ml.git
cd fetch_ml
# Start development stack with monitoring
make dev-up
# Run basic tests
make test-unit
# Then follow the Quick Start guide
# docs/src/quick-start.md
Quick Navigation
Getting Started
- Getting Started Guide - Complete setup instructions
- Simple Install - Quick installation guide
Security & Authentication
- Security Overview - Security best practices
- API Key Process - Generate and manage API keys
- User Permissions - Role-based access control
Configuration
- Environment Variables - Configuration options
- Smart Defaults - Default configuration settings
Development
- Architecture - System architecture and design
- CLI Reference - Command-line interface documentation
- Testing Guide - Testing procedures and guidelines
- Jupyter Workflow - CLI and Jupyter integration
- Queue System - Job queue implementation
Production Deployment
- Deployment Guide - Production deployment instructions
- Performance & Monitoring - Monitoring and observability
- Operations Guide - Production operations
Features
- Secure Authentication - RBAC with API keys, roles, and permissions
- Containerized - Podman-based secure execution environments
- Database Storage - SQLite backend for user management (optional)
- Audit Trail - Complete logging of all actions
- Production Ready - Security audits, systemd services, log rotation
Need Help?
- Documentation: Use the navigation menu on the left
- Quick help:
make help - Tests:
make test-unit
Happy ML experimenting!