Free Open-source ML observability course for data scientists and ML engineers. Learn how to monitor and debug your ML models in production.

A comprehensive solution for monitoring your AI models in production

A Python client to interact with Arize API

A Java client to interact with Arize API

Example projects for Arthur Model Monitoring Platform

Enterprise-grade machine learning observability platform that detects data drift, concept drift, and performance degradation in production models. Features statistical drift detection (KS test, PSI), real-time alerting, Redis caching, and FastAPI backend.

Local-first ML observability — track runs, metrics, and environments without a cloud backend.

Autonomous multi-agent system for ML infrastructure reliability. Detects, diagnoses, and remediates production ML incidents through a coordinated Sentry → Sleuth → Medic → Scribe agent pipeline. Built with Google ADK + Gemini, FastAPI, and a live failure-injection demo harness.

Aporia — AI guardrails and observability (acquired by Coralogix)