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MLflow 2.9.0

· 3 min read
MLflow maintainers

MLflow 2.9.0 includes several major features and improvements.

MLflow AI Gateway deprecation (#10420, @harupy)

The feature previously known as MLflow AI Gateway has been moved to utilize the MLflow deployments API. For guidance on migrating from the AI Gateway to the new deployments API, please see the [MLflow AI Gateway Migration Guide](https://mlflow.org/docs/latest/llms/gateway/migration.html.

MLflow Tracking docs overhaul (#10471, @B-Step62)

The MLflow tracking docs have been overhauled. We'd like your feedback on the new tracking docs!

Security fixes

Three security patches have been filed with this release and CVE's have been issued with the details involved in the security patch and potential attack vectors. Please review and update your tracking server deployments if your tracking server is not securely deployed and has open access to the internet.

  • Sanitize path in HttpArtifactRepository.list_artifacts (#10585, @harupy)
  • Sanitize filename in Content-Disposition header for HTTPDatasetSource (#10584, @harupy).
  • Validate Content-Type header to prevent POST XSS (#10526, @B-Step62)

Features

Bug fixes

  • [Tracking] Resume system metrics logging when resuming an existing run (#10312, @chenmoneygithub)
  • [UI] Fix incorrect sorting order in line chart (#10553, @B-Step62)
  • [UI] Remove extra whitespace in git URLs (#10506, @mrplants)
  • [Models] Make spark_udf use NFS to broadcast model to spark executor on databricks runtime and spark connect mode (#10463, @WeichenXu123)
  • [Models] Fix promptlab pyfunc models not working for chat routes (#10346, @daniellok-db)

Documentation updates

For a comprehensive list of changes, see the release change log, and check out the latest documentation on mlflow.org.