model-monitoring

Free MLOps course from DataTalks.Club

Evidently is ​​an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.

Open-source observability for your LLM application, based on OpenTelemetry

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

Sister project to OpenLLMetry, but in Typescript. Open-source observability for your LLM application, based on OpenTelemetry

⚓ Eurybia monitors model drift over time and securizes model deployment with data validation

[Not Actively Maintained] Whitebox is an open source E2E ML monitoring platform with edge capabilities that plays nicely with kubernetes

MLOps workshop with Amazon SageMaker

The official python package for NimbleBox. Exposes all APIs as CLIs and contains modules to make ML 🌸

A toolkit for evaluating and monitoring AI models in clinical settings

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

🚀 Stream inferences of real-time ML models in production to any data lake (Experimental)

A curated list of awesome open source tools and commercial products for monitoring data quality, monitoring model performance, and profiling data 🚀

Version, share, deploy, and monitor models.

High-scale LLM gateway, written in Rust. OpenTelemetry-based observability included

A python library to send data to Arize AI!

"1 config, 1 command from Jupyter Notebook to serve Millions of users", Full-stack On-Premises MLOps system for Computer Vision from Data versioning to Model monitoring and drift detection.

Experiments with Model Training, Deployment & Monitoring

A modern, enterprise-ready business intelligence web application