featureform VS feast

Compare featureform vs feast and see what are their differences.

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featureform feast
28 8
1,667 5,216
1.4% 2.7%
9.7 9.3
2 days ago 1 day ago
Jupyter Notebook Python
Mozilla Public 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.

featureform

Posts with mentions or reviews of featureform. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-10-13.

feast

Posts with mentions or reviews of feast. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-06-22.

What are some alternatives?

When comparing featureform and feast you can also consider the following projects:

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

Milvus - A cloud-native vector database, storage for next generation AI applications

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

great_expectations - Always know what to expect from your data.

mlrun - Machine Learning automation and tracking

feathr - Feathr – A scalable, unified data and AI engineering platform for enterprise

clearml - ClearML - Auto-Magical CI/CD to streamline your ML workflow. Experiment Manager, MLOps and Data-Management

streamlit - Streamlit — A faster way to build and share data apps.

BentoML - Build Production-Grade AI Applications

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.