Learn how to design, develop, deploy and iterate on production-grade ML applications.
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Updated
Dec 7, 2023 - Jupyter Notebook
Learn how to design, develop, deploy and iterate on production-grade ML applications.
📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
Always know what to expect from your data.
The Open Source Feature Store for Machine Learning
OpenMetadata is a unified platform for discovery, observability, and governance powered by a central metadata repository, in-depth lineage, and seamless team collaboration.
The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
The open-source tool for building high-quality datasets and computer vision models
Learn how to design, develop, deploy and iterate on production-grade ML applications.
Infinitely scalable, event-driven, language-agnostic orchestration and scheduling platform to manage millions of workflows declaratively in code.
lakeFS - Data version control for your data lake | Git for data
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
Feathr – A scalable, unified data and AI engineering platform for enterprise
Compare tables within or across databases
⚡ Data quality testing for the modern data stack (SQL, Spark, and Pandas) https://www.soda.io
re_data - fix data issues before your users & CEO would discover them 😊
An open-source data logging library for machine learning models and data pipelines. 📚 Provides visibility into data quality & model performance over time. 🛡️ Supports privacy-preserving data collection, ensuring safety & robustness. 📈
First open-source data discovery and observability platform. We make a life for data practitioners easy so you can focus on your business.
A tool to help improve data quality standards in observational data science.
The Virtual Feature Store. Turn your existing data infrastructure into a feature store.
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