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MLflow 2.20.0rc0

· 2 min read
MLflow maintainers

MLflow 2.20.0rc0 is a release candidate for 2.20.0. To install, run the following command:

pip install mlflow==2.20.0rc0

Major New Features​

  • 💡Type Hint-Based Model Signature: Define your model's signature in the most Pythonic way. MLflow now supports defining a model signature based on the type hints in your PythonModel's predict function, and validating input data payloads against it. (#14182, #14168, #14130, #14100, #14099, @serena-ruan)

  • 🧠 Bedrock / Groq Tracing Support: MLflow Tracing now offers a one-line auto-tracing experience for Amazon Bedrock and Groq LLMs. Track LLM invocation within your model by simply adding mlflow.bedrock.tracing or mlflow.groq.tracing call to the code. (#14018, @B-Step62, #14006, @anumita0203)

  • 🗒️ Inline Trace Rendering in Jupyter Notebook: MLflow now supports rendering a trace UI within the notebook where you are running models. This eliminates the need to frequently switch between the notebook and browser, creating a seamless local model debugging experience. (#13955, @daniellok-db)

  • ⚡️Faster Model Validation with uv Package Manager: MLflow has adopted uv, a new Rust-based, super-fast Python package manager. This release adds support for the new package manager in the mlflow.models.predict API, enabling faster model environment validation. Stay tuned for more updates! (#13824, @serena-ruan)

  • 🖥️ New Chat Panel in Trace UI: THe MLflow Trace UI now shows a unified chat panel for LLM invocations. The update allows you to view chat messages and function calls in a rich and consistent UI across LLM providers, as well as inspect the raw input and output payloads. (#14211, @TomuHirata)

Other Features:​

  • Introduced ChatAgent base class for defining custom python agent (#13797, @bbqiu)
  • Supported Tool Calling in DSPy Tracing (#14196, @B-Step62)
  • Added support for invokers rights in Databricks Resources (#14212, @aravind-segu)
  • Applied timeout override to within-request local scoring server for Spark UDF inference (#14202, @BenWilson2)
  • Supported dictionary type for inference params (#14091, @serena-ruan)
  • Makd context parameter optional for calling PythonModel instance (#14059, @serena-ruan)
  • Set default task for ChatModel (#14068, @stevenchen-db)

Please try it out and report any issues on the issue tracker!