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MLflow 2.7.0

· 2 min read
MLflow maintainers

MLflow 2.7.0 includes several major features and improvements

  • [UI / Gateway] We are excited to announce the Prompt Engineering UI. This new addition offers a suite of tools tailored for efficient prompt development, testing, and evaluation for LLM use cases. Integrated directly into the MLflow AI Gateway, it provides a seamless experience for designing, tracking, and deploying prompt templates. To read about this new feature, see the documentation at https://mlflow.org/docs/latest/llms/prompt-engineering.html (#9503, @prithvikannan)

Features:

  • [Gateway] Introduce MosaicML as a supported provider for the MLflow AI Gateway (#9459, @arpitjasa-db)
  • [Models] Add support for using a snapshot download location when loading a transformers model as pyfunc (#9362, @serena-ruan)
  • [Server-infra] Introduce plugin support for MLflow Tracking Server authentication (#9191, @barrywhart)
  • [Artifacts / Model Registry] Add support for storing artifacts using the R2 backend (#9490, @shichengzhou-db)
  • [Artifacts] Improve upload and download performance for Azure-based artifact stores (#9444, @jerrylian-db)
  • [Sagemaker] Add support for deploying models to Sagemaker Serverless inference endpoints (#9085, @dogeplusplus)

Bug fixes:

  • [Gateway] Fix a credential expiration bug by re-resolving AI Gateway credentials before each request (#9518, @dbczumar)
  • [Gateway] Fix a bug where search_routes would raise an exception when no routes have been defined on the AI Gateway server (#9387, @QuentinAmbard)
  • [Gateway] Fix compatibility issues with pydantic 2.x for AI gateway (#9339, @harupy)
  • [Gateway] Fix an initialization issue in the AI Gateway that could render MLflow nonfunctional at import if dependencies were conflicting. (#9337, @BenWilson2)
  • [Artifacts] Fix a correctness issue when downloading large artifacts to fuse mount paths on Databricks (#9545, @BenWilson2)

Documentation updates:

  • [Docs] Add documentation for the Giskard community plugin for mlflow.evaluate (#9183, @rabah-khalek)

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