---
title: 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!

```bash
# 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](quick-start.md) - Complete setup instructions
- [Simple Install](installation.md) - Quick installation guide

### Security & Authentication
- [Security Overview](security.md) - Security best practices
- [API Key Process](api-key-process.md) - Generate and manage API keys
- [User Permissions](user-permissions.md) - Role-based access control

### Configuration
- [Environment Variables](environment-variables.md) - Configuration options
- [Smart Defaults](smart-defaults.md) - Default configuration settings

### 🛠️ Development
- **[Architecture](architecture.md)** - System architecture and design
- **[Scheduler Architecture](scheduler-architecture.md)** - Job scheduler and service management
- **[CLI Reference](cli-reference.md)** - Command-line interface documentation
- **[Testing Guide](testing.md)** - Testing procedures and guidelines
- **[Jupyter Workflow](jupyter-workflow.md)** - Jupyter notebook services
- **[vLLM Workflow](vllm-workflow.md)** - LLM inference services
- **[Queue System](queue.md)** - Job queue implementation

### Production Deployment
- [Deployment Guide](deployment.md) - Production deployment instructions
- [Performance & Monitoring](performance-monitoring.md) - Monitoring and observability
- [Operations Guide](operations.md) - 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!