UpTrain – Open-source ML observability and refinement tool
A couple of months ago, we left our jobs to build UpTrain AI, an open-source machine learning observability and refinement tool which helps users understand the performance of their models in production and improves them over time by identifying problematic data-points for retraining. Data drift, Distribution shifts, Model degradation, Edge cases - we have personally faced these problems in our previous organizations and have built a lot of tooling to solve them. We are building UpTrain so that others don’t need to build them and can solely focus on improving their ML models while we…
UpTrain – Open-source ML observability and refinement tool launched on January 25, 2023 with 88 votes, #547 of 3,901 launches that month and more than 90% of that year's launches. The site was still online in September 2026, 3 years on.
In plain words
UpTrain is an open-source tool for monitoring and improving machine learning models in production. It helps users detect data drift, distribution shifts, model degradation, and edge cases, then identifies problematic data points for retraining. Designed for machine learning teams, UpTrain automates the observability and refinement process so developers can focus on model improvement rather than building monitoring infrastructure from scratch. The tool is available free on GitHub under the Apache 2.0 license.
written from the facts on this page · September 2026
From the sources
UpTrain is an open-source unified platform to evaluate and improve Generative AI applications. We provide grades for 20+ preconfigured checks (covering language, code, embedding use-cases), perform root cause analysis on failure cases and give insights on how to resolve them. - uptrain-ai/uptrain
In the maker’s words, at launch
A couple of months ago, we left our jobs to build UpTrain AI, an open-source machine learning observability and refinement tool which helps users understand the performance of their models in production and improves them over time by identifying problematic data-points for retraining. Data drift, Distribution shifts…
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