MLflow 2.14.0 includes several major features and improvements that we're very excited to announce!
Major features:
MLflow Tracing: Tracing is powerful tool designed to enhance your ability to monitor, analyze, and debug GenAI applications by allowing you to inspect the intermediate outputs generated as your application handles a request. This update comes with an automatic LangChain integration to make it as easy as possible to get started, but we've also implemented high-level fluent APIs, and low-level client APIs for users who want more control over their trace instrumentation. For more information, check out the guide in our docs!
Unity Catalog Integration: The MLflow Deployments server now has an integration with Unity Catalog, allowing you to leverage registered functions as tools for enhancing your chat application. For more information, check out this guide!
OpenAI Autologging: Autologging support has now been added for the OpenAI model flavor. With this feature, MLflow will automatically log a model upon calling the OpenAI API. Each time a request is made, the inputs and outputs will be logged as artifacts. Check out the guide for more information!
Other Notable Features:
[Models] Support input images encoded with b64.encodebytes (#12087, @MadhuM02)
MLflow 2.14.0 includes several major features and improvements that we're very excited to announce!
Major features:
MLflow Tracing: Tracing is powerful tool designed to enhance your ability to monitor, analyze, and debug GenAI applications by allowing you to inspect the intermediate outputs generated as your application handles a request. This update comes with an automatic LangChain integration to make it as easy as possible to get started, but we've also implemented high-level fluent APIs, and low-level client APIs for users who want more control over their trace instrumentation. For more information, check out the guide in our docs!
Unity Catalog Integration: The MLflow Deployments server now has an integration with Unity Catalog, allowing you to leverage registered functions as tools for enhancing your chat application. For more information, check out this guide!
OpenAI Autologging: Autologging support has now been added for the OpenAI model flavor. With this feature, MLflow will automatically log a model upon calling the OpenAI API. Each time a request is made, the inputs and outputs will be logged as artifacts. Check out the guide for more information!
Other Notable Features:
[Models] Support input images encoded with b64.encodebytes (#12087, @MadhuM02)
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Bumps mlflow from 2.13.1 to 2.14.1.
Release notes
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Changelog
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Commits
02ee083
Runpython3 dev/update_mlflow_versions.py pre-release ...
(#12421)035822e
Do not create empty object for empty directory in S3 artifact repository (#12...d491995
Fix params and model_config handling for llm/v1/xxx Transformers model (#12401)67eca6b
Simplify deprecation message for registry stage transition (#12403)ed02f2d
Add link to langchain autologging page in doc (#12398)5fbedc9
Update New Features page (#12397)5329f72
Fix duplicate UC integration links (#12396)7fc22c4
Add link to Unity Catalog documentation in UC integration page (#12394)a9b9bfb
Fix dark mode user preference (#12386)e09f559
Add documentation for Models from Code (#12381)Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting
@dependabot rebase
.Dependabot commands and options
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