Showing 1-20 of 20 projects
Learn to build production-grade ML applications with code and best practices
Curated resources for data science and machine learning in production
A Python library for fast, customizable, and interactive data profiling and exploratory data analysis.
An open-source library for data-centric AI with tools for data quality and machine learning on messy, real-world data.
A Python library that helps ensure data quality and reliability through data profiling and testing.
Refine high-quality datasets and visual AI models with this Python library for active learning and data curation.
A unified metadata platform for data discovery, data observability, and data governance.
Evidently is an open-source ML and LLM observability framework to evaluate, test, and monitor AI-powered systems.
An open-source feature store for AI/ML applications
lakeFS is a Git-like version control system for data lakes, enabling data engineers to manage data versioning and data quality.
A Python library for comparing data across databases, supporting various database engines.
An open-source data logging library for machine learning models and data pipelines.
The Virtual Feature Store that turns existing data infrastructure into a feature store for machine learning.
Feathr is a scalable, unified data and AI engineering platform for enterprises, with features like feature engineering, feature governance, and a feature marketplace.
A data quality and observability tool for monitoring and fixing data issues before they become problems.
Scalable data pre processing and curation toolkit for Large Language Models (LLMs)
First open-source data discovery and observability platform for data practitioners.
Automatically find issues in image datasets and practice data-centric computer vision.
A curated list of resources for data-centric AI development, including tools, frameworks, and best practices.
Data quality assessment and reporting tool for data frames and database tables in R
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