Marquez
Empowering machine learning teams with improved observability and reproducibility through Marquez's open-source dataflow management system.
Marquez is an incubation project under the LF AI & Data Foundation, creating an open-source dataflow management system for machine learning workflows with improved observability and reproducibility. They develop and maintain the Marquez platform, which offers metadata management through their API and is compatible with various OpenLineage versions. With a robust foundation, active development, and notable adopters like astronomer.io and northwesternmutual.com, Marquez welcomes community contributions to enhance their innovative machine learning pipeline management system.
About Marquez
Marquez is an incubation project under active development, sponsored by the LF AI & Data Foundation. The team at Marquez is dedicated to creating a dataflow management system, aimed at improving the observability and reproducibility of machine learning workflows.
They develop and maintain the Marquez platform, which provides metadata management for machine learning pipelines through their open-source API. Their mission aligns with the OpenLineage project, an open standard for metadata and lineage collection. Marquez is known for its compatibility with various versions of OpenLineage, including the current, recommended, and maintenance ones.
Marquez's services are built on a robust foundation, as evidenced by their active development status, CircleCI badge, Codecov coverage, and Maven Central repository presence. They welcome contributions from the community to ensure continuous improvement and growth. Notable adopters of Marquez include astronomer.io, datakin.com, and northwesternmutual.com.
Interested parties can engage with the Marquez team through their website, source code repository on GitHub, Slack channel, or Twitter account. Guidelines for contributing and reporting vulnerabilities are available on their contributing document and security policy, respectively. Join their community to be a part of this innovative project leveraging AI for machine learning pipeline management!