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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.

Have a technical question, or want to learn more? Please visit our documentation, MSDN forum or StackOverflow.


  1. Migration from Azure Machine Learning Studio to Visual Interface

    Ideally this should be done automatically for us, but I'd be happy with an export/import flow that lets us migrate manually.

    7 votes
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    1 comment  ·  Flag idea as inappropriate…  ·  Admin →

    Hi Azure Customer,

    Our plan is to support convert the Studio experiment to visual interface experiment. Model and web service are currently out of scope. Thank you for your feedback and please keep eyes on it.

    Regards,
    Azure CXP Community

  2. Default dependency improvement request for ReinforcementLearningEstimator instances

    The following settings of script parameters are not available in the default environment, and you must install tensorflow==2.1.0 and tensorflow_probability==0.9.0 using the pip_packages parameter.

    >training_algorithm = 'SAC'
    >rl_environment = 'Pendulum-v0'

    Since SAC is a new and very powerful algorithm, it is in high customer demand, so please make it available in the default dependency.

    6 votes
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    0 comments  ·  Flag idea as inappropriate…  ·  Admin →
  3. Monitor AML Cluster performace

    Add to AML Python SDK functions to monitor AML compute cluster's load/performance (CPU, Mem, Disk I/O, Network, GPU).

    6 votes
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    0 comments  ·  Flag idea as inappropriate…  ·  Admin →
  4. Request to make the Ray Library 1.0.1 or later available in the ReinforcementLearningEstimator class

    Let me ask you about the parameter settings for the ReinforceLearningEstimator class at the link below.

    https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-reinforcement-learning#define-a-worker-configuration
    > # Pip packages we will use for both head and worker
    > pip_packages=["ray[rllib]==0.8.3"] # Latest version of Ray has fixes for isses related to object transfers

    Setting the pip_packages parameter of the ReinforcementLearningEstimator class to ['ray[rllib]==1.0.1'] or later will result in a "no such option: --redis-port" error. I suppose this is because the Ray 1.0.1 and later doesn't support "--redis-port" parameter and the implement of ReinforcementLearningEstimator class is fixed to use "ray start --head --redis-port_6379 ~".

    I know it's a preview feature…

    6 votes
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    0 comments  ·  Flag idea as inappropriate…  ·  Admin →
  5. AKS AGIC Support for ML Studio

    Publishing from ML Studio creates its own Ingress controller, even when you have AGIC Addon/Helm enabled AKS Cluster. It would be nice to have the ML Services deploy to existing AGIC if any before creating a hole to internet.

    6 votes
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    0 comments  ·  Flag idea as inappropriate…  ·  Admin →
  6. 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.

    6 votes
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    0 comments  ·  Flag idea as inappropriate…  ·  Admin →
  7. Link Dataset, Experiment, Image, Model, Deployement

    As part of managing the whole Data Science Process within Azure ML, is there a way to link a Dataset, an Experiment and related produced Image, Model and Deployment, apart from description.

    6 votes
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    2 comments  ·  Flag idea as inappropriate…  ·  Admin →
  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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    0 comments  ·  Flag idea as inappropriate…  ·  Admin →
  9. Support python 3.7

    Support running Python 3.7 scripts

    5 votes
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    1 comment  ·  Flag idea as inappropriate…  ·  Admin →
  10. Add possibility to pass parameters to scoring script when deploying an image

    When deploying an image to AML Service, the path of the scoring script is given to the method using the executionscript parameter: ContainerImage.imageconfiguration(execution_script='score.py', ...). However, there is no argument for passing values to script parameters, which are needed in the init() function to do stuff like fetching passwords from Key Vault and getting sample data for the schema.

    Please add the possibility to pass parameters to the scoring script when deploying and image to AML Service. For example, when creating a Python script step in an AML Pipeline, we can add these parameters as follows: PythonScriptStep(scriptname='train.py', arguments=['--workspace

    5 votes
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    0 comments  ·  Flag idea as inappropriate…  ·  Admin →
  11. 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

    5 votes
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    1 comment  ·  Flag idea as inappropriate…  ·  Admin →
  12. Remove AKS minimum core restriction ("cluster_purpose")

    If I want to attach an AKS cluster in development environments that don't meet the 4 agents / 12 core requirements, I need to set the "clusterpurpose = AksCompute.ClusterPurpose.DEVTEST" parameter via the Python API. However, this parameter is not supported in ARM templates and also not in the Azure CLI extension, so this complicates our automation strategy.

    The minimum requirements for running an AKS cluster in production are well documented, so it shouldn't be up to the "Machine Learning service" to enforce any restrictions.

    Having this "cluster_purpose" parameter is just an unnecessary complication, and should therefore be removed…

    5 votes
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    0 comments  ·  Flag idea as inappropriate…  ·  Admin →
  13. Allow for filtering to experiment / error type granularity in Azure Monitoring

    Allow for filtering to experiment granularity in Azure Monitoring.

    Currently metrics only send value 1 if send to Azure Monitoring e.g. if an experiment run succeeds. During experimentation the larger share of experiments will fail, so we need to understand both the error type and the specific experiment at least.

    5 votes
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  14. We have to be able to export experiment in AML Studio so that it can be imported later with another account

    I developed an experiment in AML Studio and deployed it as a web service.

    I would really appreciate it if I can I export it so that it can be imported later with another Azure Account and it can be redeployed again (in case that i don't have my account anymore or i delete my current experiment)

    5 votes
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    1 comment  ·  Flag idea as inappropriate…  ·  Admin →
  15. Deploy same name service in Azure machine learning workspace service.

    In Aml workspace, we are allowed to add multiple Compute assets for a type. Ideally ml services should able be deploy on both AKS cluster but due to some reason, same named service can't be maintain in Deployment asserts. For example, I can have two AKS Compute resource in a workspace named as AKS1 and AKS2 and service named as 'intelligentAgent'. This service can be deploy in AKS1 but I have trouble deploying same service in AKS2.

    I need to do this to maintain services for BCP and disaster recovery.

    Could you please allow same named services on different AKS…

    5 votes
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    1 comment  ·  Flag idea as inappropriate…  ·  Admin →
  16. Azure Policy Support for restricting SKU of AML Compute

    Today, Azure ML Compute does not honor Azure Policy Restrictions on VM SKUs

    I Created an Azure Policy restricting VMs to a specific SKU(VM Size), assigned the Policy to resource group having Azure ML WorkSpace
    Still, I can create ML Compute using AzureML workspace for any size, including the size which is disallowed in the Policy
    Is this a bug or by design? I want to restrict the size of compute being created from AzureML

    5 votes
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    0 comments  ·  Flag idea as inappropriate…  ·  Admin →
  17. ML studio Experiments page to allow folders

    In ML Studio, at experiments, we need to allow a user to create folders to organize various projects. Usually one creates different experiments to tackle the problem in different ways. In the end, maybe only one or two experiments are put to use. However, the user would like to retain all prior experiments for future reference on what was tried. It also helps to allow user to add a Readme.txt to go along with each project to document. In our case, a workspace is shared, and the person who created the experiments may leave. In addition, after two years I…

    4 votes
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    1 comment  ·  Flag idea as inappropriate…  ·  Admin →
  18. 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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  19. Allow attachment of a VM to AML to private IP address

    Please allow attachment of a virtual machine to machine learning studio by using private ip address of a VM. Due to company security restrictions it is not possible to use public ip address. Also from security point of view it doesn't make sense to use public ip only.

    4 votes
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  20. Provide predict_proba for multiclass prediction models

    I have successfully trained a multi-class decision forest in ML Studio, but I would like to return all class probabilities as a 2d-array [nscores, nclasses], not just the best class prediction. This is similar to the predict_proba() function from scikit learn models. Can you provide? My customer is asking for this feature.

    4 votes
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