MLflow 2.17.0rc0 is a release candidate for 2.17.0. To install, run the following command:
pip install mlflow==2.17.0rc0
We are excited to announce the release candidate for MLflow 2.17.0. This release includes several enhancements to extend the
functionality of MLflow's ChatModel interface to further extend its versatility for handling custom GenAI application use cases.
We're also starting the work on improving both the utility and the versatility of MLflow's evaluate functionality for GenAI,
initially with support for callable GenAI evaluation metrics.
ChatModel enhancements - As the GenAI-focused 'cousin' of PythonModel, ChatModel is getting some sizable functionality
extensions. From native support for tool calling (a requirement for creating a custom agent), simpler conversions to the
internal dataclass constructs needed to interface with ChatModel via the introduction of from_dict methods to all data structures, the addition of a metadata field to allow for full input payload customization, handling of the new refusal response type, to the inclusion of the interface type to the response structure to allow for greater integration compatibility.
(#13191, #13180, #13143, @daniellok-db, #13102, #13071, @BenWilson2)
Callable GenAI Evaluation Metrics - As the intial step in a much broader expansion of the functionalities of mlflow.evaluate for
GenAI use cases, we've converted the GenAI evaluation metrics to be callable. This allows you to use them directly in packages that support callable GenAI evaluation metrics, as well as making it simpler to debug individual responses when prototyping solutions. (#13144, @serena-ruan)
Audio file support in the MLflow UI - You can now directly 'view' audio files that have been logged and listen to them from within the MLflow UI's artifact viewer pane. (#13017, @sydneyw-spotify)
MLflow AI Gateway is no longer deprecated - We've decided to revert our deprecation for the AI Gateway feature. We had renamed it to the MLflow Deployments Server, but have reconsidered and reverted the naming and namespace back to the original configuration.
We are excited to announce the release of MLflow 2.17.0! This release includes several enhancements to extend the
functionality of MLflow's ChatModel interface to further extend its versatility for handling custom GenAI application use cases.
Additionally, we've improved the interface within the tracing UI to provide a structured output for retrieved documents,
enhancing the ability to read the contents of those documents within the UI.
We're also starting the work on improving both the utility and the versatility of MLflow's evaluate functionality for GenAI,
initially with support for callable GenAI evaluation metrics.
Major Features and notifications:
ChatModel enhancements - As the GenAI-focused 'cousin' of PythonModel, ChatModel is getting some sizable functionality
extensions. From native support for tool calling (a requirement for creating a custom agent), simpler conversions to the
internal dataclass constructs needed to interface with ChatModel via the introduction of from_dict methods to all data structures,
the addition of a metadata field to allow for full input payload customization, handling of the new refusal response type, to the
inclusion of the interface type to the response structure to allow for greater integration compatibility.
(#13191, #13180, #13143, @daniellok-db, #13102, #13071, @BenWilson2)
Callable GenAI Evaluation Metrics - As the intial step in a much broader expansion of the functionalities of mlflow.evaluate for
GenAI use cases, we've converted the GenAI evaluation metrics to be callable. This allows you to use them directly in packages that support
callable GenAI evaluation metrics, as well as making it simpler to debug individual responses when prototyping solutions. (#13144, @serena-ruan)
Audio file support in the MLflow UI - You can now directly 'view' audio files that have been logged and listen to them from within the MLflow UI's
artifact viewer pane.
MLflow AI Gateway is no longer deprecated - We've decided to revert our deprecation for the AI Gateway feature. We had renamed it to the
MLflow Deployments Server, but have reconsidered and reverted the naming and namespace back to the original configuration.
Features:
[Tracing] Add Standardization to retriever span outputs within MLflow tracing (#13242, @daniellok-db)
[Models] Add support for LlamaIndex Workflows objects to be serialized when calling log_model() (#13277, #13305, #13336, @B-Step62)
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Bumps mlflow from 2.16.2 to 2.17.0.
Release notes
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Changelog
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.Dependabot commands and options
You can trigger Dependabot actions by commenting on this PR: - `@dependabot rebase` will rebase this PR - `@dependabot recreate` will recreate this PR, overwriting any edits that have been made to it - `@dependabot merge` will merge this PR after your CI passes on it - `@dependabot squash and merge` will squash and merge this PR after your CI passes on it - `@dependabot cancel merge` will cancel a previously requested merge and block automerging - `@dependabot reopen` will reopen this PR if it is closed - `@dependabot close` will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually - `@dependabot show