MLflow 2.19.0rc0 Released

[Serena Ruan]

Hi all, we are excited to announce the release of MLflow 2.19.0rc0! This release includes a number of significant features, enhancements, and bug fixes.

Major New Features

• Tracing improvements - MLflow Tracing now supports both automatic and manual tracing for DSPy, LlamaIndex and Langchain flavors. Tracing feature is also auto-enabled for mlflow evaluation for all supported flavors. (#13790, #13793, #13795, #13897, @B-Step62)
• New Tracing Integrations - MLflow Tracing now supports CrewAI and Anthropic, enabling a one-line, fully automated tracing experience. (#13903, @TomeHirata, #13851, @gabrielfu)
• Any Type in model signature - MLflow now supports AnyType in model signature. It can be used to host any data types that were not supported before. (#13766, @serena-ruan)

Other Features:

• [Tracking] Add update_current_trace API for adding tags to an active trace. (#13828, @B-Step62)
• [Deployments] Update databricks deployments to support AI gateway & additional update endpoints (#13513, @djliden)

Bug fixes:

• [Tracking] Allow passing in spark connect dataframes in mlflow evaluate API (#13889, @WeichenXu123)
• [Tracking] Fix mlflow.end_run inside a MLflow run context manager (#13888, @WeichenXu123)
• [Scoring] Fix spark_udf conditional check on remote spark-connect client or Databricks Serverless (#13827, @WeichenXu123)
• [Models] Allow changing max_workers for built-in LLM-as-a-Judge metrics (#13858, @B-Step62)
• [Models] Support saving all langchain runnables using code-based logging (#13821, @serena-ruan)

Documentation updates:

• [Docs] Add Ollama and Instructor examples in tracing doc (#13937, @B-Step62)

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