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)

Small bug fixes and documentation updates:

#12052, #12053, #12022, #12029, #12024, #11992, #12004, #11958, #11957, #11850, #11938, #11924, #11922, #11920, #11820, #11822, #11798, @serena-ruan; #12054, #12051, #12045, #12043, #11987, #11888, #11876, #11913, #11868, @sunishsheth2009; #12049, #12046, #12037, #11831, @dbczumar; #12047, #12038, #12020, #12021, #11970, #11968, #11967, #11965, #11963, #11941, #11956, #11953, #11934, #11921, #11454, #11836, #11826, #11793, #11790, #11776, #11765, #11763, #11746, #11748, #11740, #11735, @harupy; #12025, #12034, #12027, #11914, #11899, #11866, @BenWilson2; #12026, #11991, #11979, #11964, #11939, #11894, @daniellok-db; #11951, #11974, #11916, @annzhang-db; #12015, #11931, #11627, @jessechancy; #12014, #11917, @prithvikannan; #12012, @AveshCSingh; #12001, @yunpark93; #11984, #11983, #11977, #11977, #11949, @edwardfeng-db; #11973, @bbqiu; #11902, #11835, #11775, @B-Step62; #11845, @lababidi