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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. Change resource group for LB, Public IP, and NSG of Compute Instance

    Lb, public IP, and NSG, which are created when a compute instance is created behind a virtual network, are created in the same resource group as the virtual network.

    If the virtual network and AML workspace are in different resource groups, it can be difficult to understand the cost of AML.

    For the convenience of cost control, want to make improvements that LB, Public IP, and NSG are created in the same resource groups as the AML workspace or the use of a tag common to the AML workspace.

    8 votes
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    0 comments  ·  Flag idea as inappropriate…  ·  Admin →
  2. Don't have invisible meta data from scoring, that cannot be replaced

    It turns out that the Score activity in the Designer, adds some invisible meta data, called Score, that if you do any Arithmetic work on, will remove that metadata on the column, and make the Evaluator stop working. Just spent about 14 tech support calls tracking this down.

    Here is what he said:
    This is because the Evaluate Model module is expecting a ‘Scored Dataset’, which means the Score columns (predicted column, Scored Labels, Scored Probabilities) all have metadata assigned to them so that their ‘Feature type’ is Numeric/String Score instead of Numeric/String Feature. Passing the scored dataset into ‘Apply…

    8 votes
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    1 comment  ·  Flag idea as inappropriate…  ·  Admin →
  3. Add PowerShell to Azure Notebooks

    Please add and improve the kernel for Windows PowerShell/Powershell Core/6/7 to Azure Notebooks.
    PowerShell should be treated as first citizen language and there are a lot of usecases for teaching with Azure Notebooks.
    Alternatively provide an option to load custom kernels.

    7 votes
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    0 comments  ·  Flag idea as inappropriate…  ·  Admin →
  4. Enable Datastores Service in AML to access the default Storage Account using "Resource Instances" in the integrated Firewall of the SA

    Resource instances by adding Microsoft.MachineLearningServices in the integrated Firewall of the Storage Account when it's behind a Vnet enable us to authorize the workspace to write experiments output, models and logs.

    Activating the integrated firewall however blocking the Datastores of the workspace to access the storage account (such as Designer or Explore DS) in scenarios where the private endpoint is not applicable to be implemented.

    It will be a huge advantage if the Datastore uses the same Resource Instance as (or some other feature) in order to access the default storage account of the workspace privatly without the need of…

    7 votes
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    0 comments  ·  Flag idea as inappropriate…  ·  Admin →
  5. Support Python 2.x

    Please support Python 2.x as many frameworks still use it.

    7 votes
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    1 comment  ·  Flag idea as inappropriate…  ·  Admin →
  6. 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.

    7 votes
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  7. Add segmentation to data labeling tool

    You have bounding box labeling but not anything to help with segmentation.

    7 votes
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  8. 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

  9. Enable managed identity for compute instance

    For compute clusters we are able to use managed identities but not for compute instances. And there are functions for the Azure ML SDK that need authentication, especially the Workspace method, which by default assumes an interactive authentication method: https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.workspace.workspace?view=azure-ml-py#from-config-path-none--auth-none---logger-none---file-name-none-

    6 votes
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  10. 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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  11. 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 now, but please…

    6 votes
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    0 comments  ·  Flag idea as inappropriate…  ·  Admin →
  12. 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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  13. 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 →
  14. 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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  15. Support python 3.7

    Support running Python 3.7 scripts

    5 votes
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  16. 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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  17. 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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  18. 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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  19. 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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  20. 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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