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Learn how to develop, deploy and iterate on production-grade ML applications.
The Open Context Layer for Data and AI , OpenMetadata is the open platform for building trusted data context and business semantics for humans, AI assistants, and agents.
1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
Always know what to expect from your data.
Cleanlab's open-source library is the standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
Always know what to expect from your data.
Evidently is an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.
The Open Source Feature Store for AI/ML
lakeFS - Data version control for your data lake | Git for data
Data Contracts engine for the modern data stack. https://www.soda.io
Open-source ETL/ELT you deploy on your own servers or cloud. Built on DuckDB: no-code/low-code visual pipelines or SQL, 385 components, dbt, CDC, data quality, reverse ETL, lineage, MCP for AI agents. No vendor cloud, no per-row billing.
Dingo: A Comprehensive AI Data, Model and Application Quality Evaluation Tool
Engine for AI/ML/Data tracking, visualization, explainability, drift detection, and dashboards for Polyaxon.
Data validation toolkit for assessing and monitoring data quality.
Data Quality and Observability platform for the whole data lifecycle, from profiling new data sources to full automation with Data Observability. Configure data quality checks from the UI or in YAML files, let DQOps run the data quality checks daily to detect data quality issues.
Lightweight DataFrame validation decorators for Pandas, Polars, Modin, and PyArrow. No custom types required.
ETL / ELT / Reverse ETL Framework powered by DuckDB, designed to seamlessly integrate and process data from diverse sources. It leverages Markdown as a configuration medium, where YAML blocks define metadata for each data source, and embedded SQL blocks specify the extraction, transformation, and loading logic.
Declarative data quality engine. Define checks in YAML, run anywhere.
A detective for your data. Zero-config data quality monitoring — works with dbt, Postgres, BigQuery, Snowflake. No YAML.
Blazingly fast data comparison tool for Python, powered by Rust. Compare massive CSV/Parquet datasets instantly.
Open, audit-grade agentic data quality framework with portable industry packs