Hacktoberfest
Thanks for your interest in contributing to Evidently!
This page describes how you can contribute during Hacktoberfest (and beyond!).
Evidently is an open-source Python library for data scientists and ML engineers. It helps evaluate, test, and monitor the performance of ML models from validation to production.
Evidently evaluates different aspects of the data and ML model performance: from data integrity to the ML model quality. You can get the results as interactive visual dashboards in the Jupyter notebook or export them as JSON or a Python dictionary.
If you have not used Evidently before, you can go through the Getting Started tutorial. It will take you about 10 minutes to understand the basic functionality.
There are different ways how you can contribute to Evidently. You can read our Contribution Guide.
We welcome all improvements or fixes, even the tiny ones, and non-code contributions. Do you see a typo in the documentation? Don’t be shy, and send us a pull request. No contribution is too small!
In addition, during Hacktobfest, we invite you to make a specific type of contribution: help us add new statistical tests and metrics to detect data drift.
