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Eurybia Alternatives
Similar projects and alternatives to eurybia
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evidently
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.
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AppSignal
AppSignal knows why the f*#k it crashed. Stop vibe-debugging. Every exception, every backtrace, grouped so you see patterns, not noise.
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shapash
🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
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deepchecks
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.
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SaaSHub
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NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better eurybia alternative or higher similarity.
eurybia discussion
eurybia reviews and mentions
Posts with mentions or reviews of eurybia.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-05-24.
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State of the Art data drift libraries on Python?
Try out eurybia, from the author of shapash which is a brilliant library as well.
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Providing ML team with data: normalized or denormalized?
Your data scientists will cook up ugly bits of code to prepare their training data, you'll probably have to rewrite that when they want to ship to prod and also detect and handle discrepancies. In that regard, it sounds like you may enjoy Eurybia to communicate about this data with your data scientists. We made it precisely for that.
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Advice on a Data Quality framework
So we just trained a model to try and do the same, and then sort of read its entrails through Shapash. The more it can tell the difference, the more your data has changed. We can know which variable has changed the most, and how much it's important to our models. If all else fails (and also if all else works), we can still know (again, this is all quantified in some way, we need numbers, not eyeballings) how much our models predictions have evolved over time, independantly of particular data changes, legit or not. How can our models predictions change if the data is all clean, you ask ? I mean I asked, but you would have too, in my shoes. What lies beyond data engineering ? What is the meaning of life ? The answer is concept drift, and that's where we're starting to work on now that we have a good grasp on data drift. Anyways, the tool is Eurybia. If any part of my ramblings resemble some of your work, please give it a try and chat us up here or through the repo, we are of course very eager to get feedbacks and possibly even contributions, who knows. See ya !
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www.saashub.com | 7 Aug 2026
Stats
Basic eurybia repo stats
3
224
4.8
5 months ago
MAIF/eurybia is an open source project licensed under Apache License 2.0 which is an OSI approved license.
The primary programming language of eurybia is Jupyter Notebook.
Sponsored
AppSignal knows why the f*#k it crashed.
Stop vibe-debugging. Every exception, every backtrace, grouped so you see patterns, not noise.
www.appsignal.com