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MLflow 2.13.0

· 3 min read
MLflow maintainers

MLflow 2.13.0 includes several major features and improvements

With this release, we're happy to introduce several features that enhance the usability of MLflow broadly across a range of use cases.

Major Features and Improvements:​

  • Streamable Python Models: The newly introduced predict_stream API for Python Models allows for custom model implementations that support the return of a generator object, permitting full customization for GenAI applications.

  • Enhanced Code Dependency Inference: A new feature for automatically inferrring code dependencies based on detected dependencies within a model's implementation. As a supplement to the code_paths parameter, the introduced infer_model_code_paths option when logging a model will determine which additional code modules are needed in order to ensure that your models can be loaded in isolation, deployed, and reliably stored.

  • Standardization of MLflow Deployment Server: Outputs from the Deployment Server's endpoints now conform to OpenAI's interfaces to provide a simpler integration with commonly used services.

Features:

  • [Deployments] Update the MLflow Deployment Server interfaces to be OpenAI compatible (#12003, @harupy)
  • [Deployments] Add Togetherai as a supported provider for the MLflow Deployments Server (#11557, @FotiosBistas)
  • [Models] Add predict_stream API support for Python Models (#11791, @WeichenXu123)
  • [Models] Enhance the capabilities of logging code dependencies for MLFlow models (#11806, @WeichenXu123)
  • [Models] Add support for RunnableBinding models in LangChain (#11980, @serena-ruan)
  • [Model Registry / Databricks] Add support for renaming models registered to Unity Catalog (#11988, @artjen)
  • [Model Registry / Databricks] Improve the handling of searching for invalid components from Unity Catalog registered models (#11961, @artjen)
  • [Model Registry] Enhance retry logic and credential refresh to mitigate cloud provider token expiration failures when uploading or downloading artifacts (#11614, @artjen)
  • [Artifacts / Databricks] Add enhanced lineage tracking for models loaded from Unity Catalog (#11305, @shichengzhou-db)
  • [Tracking] Add resourcing metadata to Pyfunc models to aid in model serving environment configuration (#11832, @sunishsheth2009)
  • [Tracking] Enhance LangChain signature inference for models as code (#11855, @sunishsheth2009)

Bug fixes:

  • [Artifacts] Prohibit invalid configuration options for multi-part upload on AWS (#11975, @ian-ack-db)
  • [Model Registry] Enforce registered model metadata equality (#12013, @artjen)
  • [Models] Correct an issue with hasattr references in AttrDict usages (#11999, @BenWilson2)

Documentation updates:

  • [Docs] Simplify the main documentation landing page (#12017, @BenWilson2)
  • [Docs] Add documentation for the expanded code path inference feature (#11997, @BenWilson2)
  • [Docs] Add documentation guidelines for the predict_stream API (#11976, @BenWilson2)
  • [Docs] Add support for enhanced Documentation with the JFrog MLflow Plugin (#11426, @yonarbel)

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