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. define and run pipeline using Logic App

    It's super make sense this type of workflow via GUI, not only code. Please refere the following service as well.


    • Azure DevOps

    • Azure Data Factory

    3 votes
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  2. error on : FailedToEvaluateRScript: The following error occurred during evaluation of R script: R_tryEval: return error: Error: bad restore

    I have the following error on azureml :

    FailedToEvaluateRScript: The following error occurred during evaluation of R script: R_tryEval: return error: Error: bad restore file magic number (file may be corrupted) -- no data loaded , Error code: LibraryExecutionError, Http status code: 400, Timestamp: Mon, 02 Dec 2019 06:33:22 GMT

    1 vote
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  3. 3 votes
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  4. 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?

    1 vote
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  5. Add the Kolmogorov-Smirnov test within the "Filter Based Feature Selection" module

    The Kolmogorov-Smirnov test provides very important metrics for assessing the information value in each column, see https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnovtest#Two-sampleKolmogorov–Smirnov_test

    If you are interested, I can provide an Excel spreadsheet with relevant example.

    1 vote
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  6. We need an example of using AutoML in Azure DevOps rather than a simple sklearn model in the Python MLOps example?

    Pelase Add good example in AzureDevOps using a video tutorial on your websiten rather than the MLOPs Python sample and how we can use it in Azure DevOps.

    3 votes
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  7. restrict access to Storage Account when using ML

    Currently the access to the required storage account cannot be restricted when using ML. For security reasons it is wise to restrict access ("firewalls and virtual networks")

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  8. Spelling error in Azure Machine Learning Excel Add-in

    "Predicting will override exising values." should read "Predicting will override existing values."

    New product. Great stuff.

    1 vote
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  9. Any plans to for ML Studio to support Teradata?

    Any plans for ML Studio Import/Export module to support Teradata?

    1 vote
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  10. Need drop-out parameter for neural network

    The neural network for custom script should put a dropout rate parameter or at least tell us the code for doing dropout in the custom script. Thanks

    3 votes
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  11. "az ml run show -r runid" pops surprised output

    When I am using command "az ml run show -r runid",
    This the output:

    az ml run show -r b4c190b4-fd00-43ca-bf31-639221a9485c
    Error, default experiment not set and experiment name parameter not provided.
    Please provide a value for the experiment name parameter.

    The problem is I can't provide experiment name, that's the information I want to query for...

    Could we refine this command? I can provide -w workspace instead.

    3 votes
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  12. AKS cluster remove models

    Bring the ability to remove particular models, and closing the containers from deployments onto an AKS cluster without deleting the cluster

    1 vote
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  13. Default compute target

    An awesome feature has been added to the new Visual interface: when I want to run my experiment, I have to specify what kind of machine I want to run it on.

    When the experiment has to run more than once, I found only one – tiny bit – annoying feature: every time you run the experiment, you have to choose a compute target. What if I want to use the same target for the next few days, or even longer?

    I wish there was a possibility to set a default size, and use that, unless I want to scale…

    3 votes
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  14. Edit or remove experiments in Automated Machine Learning

    I just tried the Automated Machine Learning service with one of my older projects, simply out of curiosity.

    I created an experiment, with a compute setup, and then I wanted to add another compute instead. The only way it can be done is by creating a new experiment with the same name, but a different compute setup.

    Should it be possible to simply edit the experiment that was generated previously from the dashboard?

    I can see, that there is an idea of keeping the previous run results, but when there is a cancelled run, or I cannot benefit from it,…

    3 votes
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  15. My account has access to 5 tenants. It seems as if it is only checking the default tenant before telling me I do not have access to my sub.

    I am trying to run Jupyter notebooks in a ML workspace in my sandbox environment. My default tenant is our production environment. It appears as if the azureml python authentication is only checking the default tenant before telling me I do not have access to me subscription.

    1 vote
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  16. Integrate with Azure Time Series Insights

    Azure ML allows me to collect inputs going into the prediction service. Why not integrate with the awesome and powerful Azure Time Series Insights service to help me analyze how this data is changing over time?

    1 vote
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  17. 1 vote
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  18. How do i find out what is taking up my storage in the Free Azure ML Workspace?

    When I go to my Datasets, the biggest one i see is a 15MB file twice, so how is it that my Workspace says it is over the limit. Please provide a way to identify and delete whatever is taking up space in my workspace.

    1 vote
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  19. How to integrate DevOps approach with VM Notebook in AMLS ?

    Today, the platform is amazingly evolving in term of integration. We can now monitor and manage almost everything in the visual interface.

    My question is how can we improve even more this experience by adding the DevOps part (instead of having to go threw Azure Notebooks, or Visual Studio) to the Notebook VM and Jupyter environment.

    3 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.

    2 votes
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