evidently VS great_expectations

Compare evidently vs great_expectations and see what are their differences.

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evidently great_expectations
10 15
4,539 9,361
4.2% 1.9%
9.5 9.9
7 days ago 5 days ago
Jupyter Notebook Python
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.

evidently

Posts with mentions or reviews of evidently. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-05-11.

great_expectations

Posts with mentions or reviews of great_expectations. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-04-24.

What are some alternatives?

When comparing evidently and great_expectations you can also consider the following projects:

kedro-great - The easiest way to integrate Kedro and Great Expectations

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.

re_data - re_data - fix data issues before your users & CEO would discover them 😊

streamlit - Streamlit β€” A faster way to build and share data apps.

seldon-core - An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models

MLflow - Open source platform for the machine learning lifecycle

whylogs - An open-source data logging library for machine learning models and data pipelines. πŸ“š Provides visibility into data quality & model performance over time. πŸ›‘οΈ Supports privacy-preserving data collection, ensuring safety & robustness. πŸ“ˆ

ydata-profiling - 1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.

fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production

metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!

soda-core - :zap: Data quality testing for the modern data stack (SQL, Spark, and Pandas) https://www.soda.io