nannyml VS eurybia

Compare nannyml vs eurybia and see what are their differences.


Detecting silent model failure. NannyML estimates performance for regression and classification models using tabular data. It alerts you when and why it changed. It is the only open-source library capable of fully capturing the impact of data drift on performance. (by NannyML)
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nannyml eurybia
4 3
1,362 174
4.4% 1.1%
9.6 7.5
8 days ago 3 months ago
Python Jupyter Notebook
Apache License 2.0 Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.


Posts with mentions or reviews of nannyml. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning nannyml yet.
Tracking mentions began in Dec 2020.


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.

What are some alternatives?

When comparing nannyml and eurybia you can also consider the following projects:

shapash - 🔅 Shapash makes Machine Learning models transparent and understandable by everyone

cuttle-cli - Cuttle automates the transformation of your Python notebook into deployment-ready projects (API, ML pipeline, or just a Python script)

evidently - Evaluate and monitor ML models from validation to production. Join our Discord:

ydata-profiling - Create HTML profiling reports from pandas DataFrame objects

ML-For-Beginners - 12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all

Made-With-ML - Learn how to responsibly develop, deploy and maintain production machine learning applications.

deep-significance - Enabling easy statistical significance testing for deep neural networks.

barfi - Python Flow Based Programming environment that provides a graphical programming environment.

TensorFlow-Examples - TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)