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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
⚓ 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
High-scale LLM gateway, written in Rust. OpenTelemetry-based observability included
MLOps workshop with Amazon SageMaker
Free Open-source ML observability course for data scientists and ML engineers. Learn how to monitor and debug your ML models in production.
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 🌸
A toolkit for evaluating and monitoring AI models in clinical settings
🚀 Stream inferences of real-time ML models in production to any data lake (Experimental)
Version, share, deploy, and monitor models.
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
MLOps for deploying a Credit Risk model