#Model-monitoring
Showing 12 of 12 repositories tagged #model-monitoring, ranked by stars
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 GenAI or 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
High-scale LLM gateway, written in Rust. OpenTelemetry-based observability included
A toolkit for evaluating and monitoring AI models in clinical settings
A curated list of awesome open source tools and commercial products for monitoring data quality, monitoring model performance, and profiling data 🚀
The official python package for NimbleBox. Exposes all APIs as CLIs and contains modules to make ML 🌸
🚀 Stream inferences of real-time ML models in production to any data lake (Experimental)
"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.