data-quality

Learn how to responsibly develop, deploy and maintain production machine learning applications.

📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.

Create HTML profiling reports from pandas DataFrame objects

Always know what to expect from your data.

The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.

Feature Store for Machine Learning

Kestra is an infinitely scalable orchestration and scheduling platform, creating, running, scheduling, and monitoring millions of complex pipelines.

lakeFS - Data version control for your data lake | Git for data

Compare tables within or across databases

The open standard for data logging

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

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

The Virtual Feature Store. Turn your existing data infrastructure into a feature store.

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

First open-source data discovery and observability platform. We make a life for data practitioners easy so you can focus on your business.

Data quality assessment and metadata reporting for data frames and database tables

A curated, but incomplete, list of data-centric AI resources.

Automatically find issues in image datasets and practice data-centric computer vision.

Qualitis is a one-stop data quality management platform that supports quality verification, notification, and management for various datasource. It is used to solve various data quality problems caused by data processing. https://github.com/WeBankFinTech/Qualitis

Compilation of high-profile real-world examples of failed machine learning projects