Fairness Pipeline Development Toolkit Documentation

Welcome to the Fairness Pipeline Development Toolkit documentation.

This toolkit provides a unified, statistically-rigorous framework for detecting, mitigating, training, and validating fairness in ML workflows.

Contents:

• Getting Started
  • Installation
  • Quick Start
  • Next Steps
• API Reference
  • Table of Contents
  • Core Metrics
  • I/O Utilities
  • Pipeline Utilities
  • Integration & Workflow
  • Training
  • Monitoring
  • Exceptions
  • Statistical Utilities
  • Version Information
  • REST API
  • Backward Compatibility
• Integration Guide
  • Table of Contents
  • Installation & Setup
  • Integration Patterns
  • Configuration Management
  • Environment Variables
  • Best Practices
  • Troubleshooting
  • REST API & Docker
  • Next Steps
• Performance
  • Table of Contents
  • Performance Benchmarks
  • Performance Characteristics
  • Optimization Tips
  • Scalability Considerations
  • Memory Usage
  • CI/CD Integration
  • Best Practices
  • Troubleshooting Performance Issues
  • Additional Resources
  • Performance Comparison
• ADR-001: Measurement Module Architecture
  • Decision
  • Consequences
  • Status:
• ADR-002: fairpipe Shim Namespace
  • Context
  • Decision
  • Consequences
  • Status:
• Versioning
  • Overview
  • Semantic Versioning
  • Backward Compatibility Guarantees
  • Deprecation Policy
  • Public API Boundaries
  • Configuration File Compatibility
  • CLI Compatibility
  • Data Format Compatibility
  • Python Version Support
  • Migration Guides
  • Version Information
  • Release Process
  • Pre-1.0.0 Considerations
  • Best Practices for Users
  • Support and Questions
  • Summary
• Feedback
  • How to Provide Feedback
  • Feedback Form
  • Feedback Review Process
  • Feedback Categories
  • Best Practices for Feedback
  • Response Times
  • Recognition
  • Questions?

Indices and tables

• Index

• Module Index

• Search Page