awesome-mlops/awesome-ml-monitoring
A curated list of awesome open source tools and commercial products for monitoring data quality, monitoring model performance, and profiling data 🚀
Staying on top of your machine learning models' performance and the quality of data feeding them is crucial after they've been deployed. This project provides a curated list of tools that help you monitor your ML models and data, identify issues like data drift or model decay, and get insights into why they might be underperforming. Data scientists, MLOps engineers, and analytics professionals will find this useful for maintaining healthy ML systems.
No commits in the last 6 months.
Use this if you need to choose an open-source or commercial tool to continuously monitor the health and performance of your machine learning models and the data pipelines that feed them.
Not ideal if you are looking for a guide on how to build machine learning models or want to explore general data visualization tools unrelated to ML monitoring.
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May 07, 2024
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