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.

nannyml: post-deployment data science in python

Frouros: an open-source Python library for drift detection in machine learning systems.

A toolkit for evaluating and monitoring AI models in clinical settings

A comprehensive solution for monitoring your AI models in production

Online and batch-based concept and data drift detection algorithms to monitor and maintain ML performance.

Passively collect images for computer vision datasets on the edge.

A tiny framework to perform adversarial validation of your training and test data.

A ⚡️ Lightning.ai ⚡️ component for train and test data drift detection

Drift Lens Demo

Real-time anomaly detection system for GitHub activity using Airflow, MLflow, and Terraform

A lightweight, no-install, GUI-based Python toolbox for detecting, measuring, and visualizing domain shift (data shift) across datasets — all running in your browser on Windows, macOS, or Linux.

Adversarial labeller is a sklearn compatible instance labelling tool for model selection under data drift.

In this project, we illustrate how the Kolmogorov Smirnov (KS) statistical test works, and why it is commonly used in Machine Learning (ML), Deep Learning (DL) and Artificial Intelligence (AI).

Tabular Data Drift Detector & Reporter:
A CLI tool that connects to any database or CSV, computes statistical drift (KS-test, Jensen-Shannon) between “baseline” vs. “current” data, and emits a Markdown/HTML report with charts.

Temporal partitioning and backtesting for time-correlated datasets

Scoring-style tree binning + PSI (numeric & categorical) + pairwise z-score bin stability over time, in an interactive UI. CLI for CI / cron. Live demo: https://dqt.gorev.space

AI-based intrusion detection research platform with FastAPI, LightGBM, synthetic network-flow generation, SHAP explainability, and drift monitoring.

A complete end-to-end machine learning pipeline to predict employee salaries (USD) using job-related features. Includes automated EDA, robust preprocessing, ML model training with MLflow, drift detection with Evidently AI, and a Flask-based web app for both single and batch predictions.

A self-healing MLOps retraining pipeline for fraud detection. Automatically detects data drift with Evidently AI and retrains the model — no human intervention required. Built with scikit-learn, Feast, and GitHub Actions.