Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.

scores: Metrics for the verification, evaluation and optimisation of forecasts, predictions or models.

Forecast Verification/Validation Tools in Python

ML model predicting League of Legends match outcomes with 87.9% accuracy pre-game. Outperforms 50% ELO baseline utilizing historic match data and machine learning.

Automated validation toolkit for tabular ML models in finance and regulated domains.

CogniBench is a software framework for benchmarking cognitive models

Framework for probing tasks.

Model versioning using Weight&Biases with Python.

Calibration of the significant Social Force Parameters in Vissim

It is the compatibility engine behind the SOTA LabVIEW Deep Learning Toolkit, ensuring that every ONNX operator behaves consistently across hardware targets. It validates each node against multiple execution providers to guarantee reliable and predictable AI deployment.

MLBuild enforces inference performance SLAs in CI, automatically blocking slow ML models before they reach production.

Open-source, reproducible simulator for analyzing quantum error correction under explicit noise assumptions, with Monte Carlo and analytical (binomial) validation, including subplot with log-scaled explicit error analysis; SEE README

Model validation for GitHub pull requests — regression detection, data quality checks, and model cards in one Action. No server required.

Model governance for insurance pricing — PRA SS1/23 validation reports, model risk management, risk tier scoring

XLChek is an offline, read-only Excel analysis tool that identifies structural risks in spreadsheets. Excel audit. Excel risk analysis. Spreadsheet diagnostics. Circular reference detection. Excel formula analyzer. Financial model audit. Offline Excel analyzer.

Meta-segmented cross-validation (Matlab & python implementation)

ML release-gate checks for drift, performance regression, latency, and JSON/Markdown deployment reports.

statistical tests for drift detection and dataset shift

Data validation using the strong pydantic framework

ML model validation and monitoring toolkit — drift detection, bias auditing, and compliance reporting