VV4ML Documentation

VV4ML is a Python library designed to streamline the verification and validation (V&V) process for machine learning models.

Why VV4ML?

VV4ML makes it as easy as possible to implement advanced techniques to ensure a specific ML model is verified, robust, and credible. Its primary aim is to provide excellent accessibility and straightforward implementation for users who want to build trustworthy AI systems without needing to be experts in formal verification.

Whether you are testing for fairness, evaluating robustness against unexpected inputs, or validating model performance across different data slices, VV4ML provides the tools you need through a clean and intuitive API.

Key Features

• Ease of Use: An intuitive Python API designed specifically for data scientists and ML engineers.
• Comprehensive V&V Tools: Ready-to-use modules for assessing model credibility, robustness, and fairness.
• Seamless Integration: Designed to work smoothly with your existing Machine Learning pipelines.

Where to go from here?

• 🛠️ Installation Guide: Get VV4ML set up on your machine using pip or uv.
• ⚡ Quickstart: See VV4ML in action and run your first programmatic Smoothness test.
• 📋 Configuration Guide: Learn how to define your entire V&V pipeline using structured YAML files.
• 🔬 Verification Modules: Dive deep into the mathematics and parameters of Smoothness, Convergence, and Numerical Error.