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.

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.

Framework for probing tasks.

CogniBench is a software framework for benchmarking cognitive models

Model versioning using Weight&Biases with Python.

Calibration of the significant Social Force Parameters in Vissim

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

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.

This project aims to explore concepts that I have only read about so far, like principal component analysis. Most of these tools come from the sklearn library, so I have the benefit of exploring this useful library. We will be using the data set from the Kaggle housing competition to build a regressor model that predicts the sale price of houses.

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)

🚀 Convert YOLO11 PyTorch models to ONNX format and validate their performance with detailed comparisons and visualizations.

Data validation using the strong pydantic framework

🧪 Test ONNX Runtime Execution Provider coverage with real-world operator support mappings for effective AI deployment insights.

A tool for validating machine learning models

Hands-on practice repo for understanding Pydantic, type safety, data validation, and clean Python data models.

An alternative (smarter) way to perform Cross Validation

Production-minded evaluation harness for LLM features with structured outputs. Includes schema validation, regression testing, and repeatable run reports.