Machine Learning

Welcome! The Azure Machine Learning team invites you to share and vote for features to help you build, manage, and deploy custom machine learning models.

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  1. We need to build in algorithms for Multi-label classification. Currently we do not support any of BP-MLL or ML-kNN etc.

    There are no support for Multi label classification by default in ML services. Users have to write their own code in Python/R or any language and go ahead with an ensemble of models. Why dont we have any support by default?

    35 votes
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  2. Support python 3.7

    Support running Python 3.7 scripts

    3 votes
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  3. Bring your own Compute Cluster

    It would be great if there was a supported method to allow support for a local cluster or compute device. (such as Azure Stack Edge)

    1 vote
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  4. Deploy the model to Azure Functions in the Designer

    There is a request to deploy a designer-generated model to Azure Functions. Are there any plans for future development?

    3 votes
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  5. Can I use custom definition script in algorithms modules?

    So we can use Net# in this environment. Just like studio.azureml.net.

    2 votes
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  6. How to save ContainerEvents for model-deployed ACI

    Can't save ContainerEvent to Log Analytics because Azure ML can't create logging-enabled containers. It would be better to be able to get the following logs.

    https://docs.microsoft.com/en-us/azure/container-instances/container-instances-log-analytics#view-events

    3 votes
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  7. Visualize support for Designer's Train Model module

    Ml Studio (classic) was able to visualize the result, but Designer doesn't seem to be able to do it. To view the model parameters and feature weights, it's better to support Visualize.

    1 vote
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  8. Provide a means to recover code when one of the workspace resources is deleted

    When a core resource for the ML workspace is deleted, the workspace can no longer retrieve any code. Creating a means to be able to resync a new resource and retrieve the code, or implementing a backup would be another solution.

    5 votes
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  9. In the Australia East Region, "Create New" fails with the "Set up compute target" of the Designer feature.

    Create New is fixed to Standard Dv2 family by default. In the Austoralia East region, the creation fails because the quota for dv2 family is 0.
    I want you to be able to choose the virtual machine size in the future.

    1 vote
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  10. Don't silently fail when an ARM template attempts to change an unchangable parameter

    When an ARM template attempts to change a workspace parameter that cannot be changed (such as hbiWorkspace), the template deployment succeeds without any error or notice even though the parameter specified in the template has not taken effect.

    This is counterintuitive at best, and also inconsistent with how other Azure resources work: those do throw an error when you try to change an unchangable parameter using an ARM template.

    Please fix this by always failing the ARM template deployment when the template tries to change an unchangable parameter.

    4 votes
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  11. Sharing compute instance in members of same developer team

    Actually compute instance can only be used by one member of team, and others developers can't use this.

    A good feature it's all members of a workspace can use compute instance (with the right rights), and make a queue of experiments runnings.

    4 votes
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  12. Request for capability to download the Bias and Feature Weights from the Trained Model

    Request for capability to download the Bias and Feature Weights from the Trained Model

    1 vote
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  13. Automatic Shutdown for Compute VMs

    It would be great if we could have the ability to automatically shut down Compute VM's at a specified time - similar to what you can do with VM's provisioned outside of the AML Portal.

    1 vote
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  14. Document azure devops machine learning tasks

    To create azure devops pipelines implementing MLOps(e.g. [1], you have created pipeline tasks which can be executed in the devops pipeline, e.g ms-air-aiagility.vss-services-azureml.azureml-restApi-task.MLPublishedPipelineRestAPITask@0 and others. There clearly mirror the CLI and API, but they provide no documentation. They should.

    1: https://github.com/microsoft/MLOpsPython/blob/master/.pipelines/diabetes_regression-ci.yml

    1 vote
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  15. Gantt view of all experiments execution in Machine Learning Workspace

    To see a Gantt chart of the time consumed by each of the experiments running simultaneously in the ML.

    2 votes
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  16. Passthrough authentication for Azure ML Datastores and Datasets

    Currently the Datastores and Datasets in Azure ML Workspace can be used by all members of the Workspace. Only the Datastore/Dataset creator needs access to the source data. This can be problematic in cases when deciding the Workspace scope in enterprise environments.

    Please add option to use passthrough authentication in Datastores & Datasets for more granular control of access rights.

    1 vote
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  17. Please replace the UserVoice forum software used here for posting feedback

    Please replace the UserVoice forum software used here for posting feedback.

    It is user-unfriendly, a torture to work with, not maintained, lacks documentation and can likely never be fixed. It seems that it's well past the point where improvements can be made to this system.

    It's beyond me to understand why the new Microsoft (which just organized an amazingly successful MSBuild 2020) allows itself to be associated with such a poor user experience.

    1 vote
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  18. az ml model (CLI) deploy/package without "--model" argument

    With Python SDK, you can both deploy and package models passing an empty list as argument of "models".

    Like:


    package = Model.package(ws, [], inference_config)
    package.wait_for_creation(show_output=True)

    Why can't we do the same thing with CLI?

    1 vote
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  19. Segregate a Workspace across multiple projects.

    Allow us to organize 1 workspace in several projects, allowing greater governance and cost sharing between projects.
    In this case, make it possible for us to create project folders where each folder / project has its associated objects, where we can implement access profiles for its objects.
    We would continue sharing processing clusters, but we could segregate registered models, pipelines, datasets, etc. by project folders.

    Today, as it is, we only achieved this segregation by assigning 1 workspace for each project.
    And this strategy of 1 workspace per project will make projects more expensive, because today, a trainning cluster cannot…

    5 votes
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  20. Automated ML UI - Featurisation Setting Setting greyed out for Time Series

    I can't select the featurisation setting under automated ml from the UI for Time Series Forecasting but can for classification and regression.

    1 vote
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