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Support for mlflow 3.x.x in azure machine learning studio?

Berthold, Jan 20 Reputation points
2026-05-07T09:39:36.12+00:00

We had the same issue that is mentioned here: https://learn.microsoft.com/en-us/answers/questions/2288377/how-to-fix-model-logging-in-mlflow or here: https://learn.microsoft.com/en-us/answers/questions/5609748/support-for-mlflow-3-x-x-in-azure-ml.

Is there any updates if mlfow 3.x. api will be available in aml in the near future ?

Best Jan

Azure Machine Learning
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  1. Amira Bedhiafi 41,641 Reputation points MVP Volunteer Moderator
    2026-05-07T10:16:51.15+00:00

    Hello Jan !

    Thank you for posting on MS Learn Q&A.

    At the moment, AML does not appear to have full MLflow 3.x server/API compatibility especially for MLflow 3 features such as the logged_models endpoint.

    I think the logged_models endpoint is not supported in Azure ML yet and downgrading is currently the only option if your workflow depends on that endpoint. Azure ML still supports the established MLflow integration for tracking, logging, model registration, and deployment, but the newer MLflow 3-specific capabilities are not fully covered by Azure ML today.

    There is also no public ETA in the Azure ML documentation for full MLflow 3.x server support.

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  1. SRILAKSHMI C 18,225 Reputation points Microsoft External Staff Moderator
    2026-05-07T14:57:36.5966667+00:00

    Hello @Berthold, Jan

    Thank you for Reaching out Microsoft Q&A.

    At present, Azure Machine Learning’s native MLflow integration is primarily aligned with the MLflow 2.x API surface, and full support for MLflow 3.x functionality is not yet generally available across all Azure ML-integrated MLflow endpoints and features.

    The behavior you referenced (including the 404/API compatibility issues discussed in the linked threads) is currently a known limitation. Azure ML today continues to rely on the existing MLflow-compatible tracking endpoints (for example /api/2.0/mlflow/...), which means some newer MLflow 3.x APIs and workflows are not yet fully supported.

    Current support status

    The following MLflow capabilities continue to work in Azure ML:

    • Experiment tracking
    • Metrics and parameter logging
    • Artifact logging
    • Model registration and deployment
    • Standard MLflow 2.x tracking workflows

    However, some MLflow 3.x-specific functionality may not yet work correctly, including:

    • logged_models
    • newer registry APIs/views
    • certain GenAI tracing/evaluation APIs
    • some MLflow 3.x client expectations/endpoints

    This is why using an MLflow 3.x client against an Azure ML workspace can currently result in issues such as:

    • 404 responses
    • unsupported endpoint errors
    • model logging incompatibilities

    Current recommendation / workaround

    As a temporary workaround, the current recommendation is to use an MLflow client version below 3.0, for example:

    pip install "mlflow<3.0"
    

    and continue using the documented MLflow 2.x-compatible workflows in Azure ML.

    At this time, there is no publicly announced ETA for complete MLflow 3.x support or full API parity in Azure Machine Learning Studio.

    Please refer this

    Track local and remote runs with MLflow: https://docs.microsoft.com/azure/machine-learning/how-to-use-mlflow

    MLflow compatibility in Azure ML: https://docs.microsoft.com/azure/machine-learning/concept-mlflow?view=azureml-api-2

    I Hope this helps. Do let me know if you have any further queries.


    If this answers your query, please do click Accept Answer and Yes for was this answer helpful.

    Thank you!

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