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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. Improve model portability

    Allow easy identification of all of an ML studio models components and exporting of these to new workspaces. Currently copying has major limitations such as data set size, workspace location etc. Datasets get duplicated when used in multiple models during the copy etc. It should be possible to select and copy multiple experiments or projects and have the copy process bring along everything that is needed including training experiments, predictive experiments, transformations and web service parameters etc. without creating duplication.

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    0 comments  ·  Flag idea as inappropriate…  ·  Admin →

    Dear Azure Customer,

    Thanks for the feedback. Your feedback is very important to us. Our engineering team will investigate it and consider your feedback seriously. We will get back to you soon.

    Thank you for understanding.
    Azure CXP Community

  2. Please provide Azure datalake as a source.

    , Either you can provide data source like you have provided in data factory where we are open to connect cross platform connection as source.

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    0 comments  ·  Flag idea as inappropriate…  ·  Admin →

    Dear Azure Customer,

    Thanks for the feedback. Your feedback is very important to us. Our engineering team will investigate it and consider your feedback seriously. We will get back to you soon.

    Thank you for understanding.
    Azure CXP Community

  3. Make warning that optimiation hasn't converged more apparent.

    The evaluation of my Multiclass Neural Network wasn't all that good. In looking to see what might have gone wrong, I came across a curious warning message in the training logs:
    /azureml-envs/azureml1f1665f56e65fc1b774765297713b3cf/lib/python3.6/site-packages/sklearn/neuralnetwork/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.

    It would be good if this warning was more obvious. I'm re-running now with >100 iterations, but wouldn't have known to do this if I didn't comb through the logs.

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    0 comments  ·  Flag idea as inappropriate…  ·  Admin →
  4. Limit of experiments in a workspace

    Is there a limit of how many experiments can we create in one workspace? Is there a limit of how many runs can we create in one experiments?

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    0 comments  ·  Flag idea as inappropriate…  ·  Admin →

    Hi Azure Customer,

    We do not have limits on pipelines runs or limits on pipelines per workspace at this point in time (not sure if we want to introduce limits as part of the SKU/billing effort).
    We have a limit on the size of a pipeline (30,000 nodes) and the numbers of schedules that will trigger pipelines per month (100,000)

    We will update our document to make it clear. Thank you.

    Regards,
    Azure CXP Community

  5. Create notebook from docker image

    I'd like to create a notebook from a docker image in my private container registry so that I can test out the pieces of my azure ml experiment that failed. It seems like you can't create a notebook with a docker image or conda environment which makes it tedious to set up a custom environment. Thanks for considering this feature!

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

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  7. tensorboard profiler is absent from azureml-tensorboard

    It's not installed when you start a cluster with azureml. To fix, pip install -U tensorboard-plugin-profile where tensorboard is run. Without this, ppl using AzureML can't easily profile memory use or IO of their models as models are training or doing inference. I can't find any guides on how to do this with Azure ML.

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  8. Where is my trained model ?

    Hello,

    I've practiced through guideline in https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-1st-experiment-sdk-train and get all expected result.

    But i cannot find the trained model file (containing the model structure, trained hyperparameters, etc. ) in workspace, besides those model.py and script.

    Where is it ?

    Thank you.

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  9. ML Services: View registered datastores and datasets in the workspace GUI blade

    The ability to be able to register datasets and datastores is really powerful. Currently, if I register datasets / datastores and their metadata with the SDK, I can't find them in the workspace GUI in the portal (where I see my experiments / runs / compute / registered models etc) - I have to list them programmatically.

    It would be great if there were a tab added to the workspace portal blade so that I could see all the datasets / datastores, the metadata I have added to them, their version history etc.

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