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

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

Drift Lens Demo

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

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.

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).

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 reusable codebase for fast data science and machine learning experimentation, integrating various open-source tools to support automatic EDA, ML models experimentation and tracking, model inference, model explainability, bias, and data drift analysis.

An ML monitoring framework, applied to an attrition risk assessment system.

Image classification with EfficientNet-B0 and the PCAM dataset. RestAPI created with Flask.

ML-focused synthetic data platform with realistic traffic patterns, seasonal effects, and temporal drift. BNPL transaction generator with risk scoring, configurable arrival patterns (Poisson, NHPP, Burst). Live API: simtom-production.up.railway.app | Day-per-second historical replay.

Scalable and production-ready MLOps pipeline using MLflow, Docker, Prometheus & GitHub Actions — featuring automated retraining and model monitoring.