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. 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.

    1 vote
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    1 comment  ·  Flag idea as inappropriate…  ·  Admin →
  2. Option to Deploy web service as HTTP instead of default HTTPS

    I need the ability to deploy my predictive model as a HTTP end point instead of the default HTTPS. There are lot of restrictions in the ERP test environment where I am building the client application that consumes the web service. An easy way to turn on/off from HTTPS to HTTP would be very useful as I migrate from Test to Prod.

    1 vote
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  3. Need reason code for model interpretability

    Can we have reason code (like the ones in DataRobot) to enable model interpretability?

    Also feature importance ranking graph?

    1 vote
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  4. Enable downloading engineered feature tables

    Model interpretability posts as an important part in ML adoption. If we can download the engineered feature tables (like what H2O.AI) offers, that will speed up our customer adoption.

    1 vote
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  5. 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).

    3 votes
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  6. 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

    3 votes
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  7. AML pipeline with ADF step

    Is there a plan to support ADF as an AML pipeline steps ?
    The use case would be to leverage the extended ADF pipeline capabilities to prepare Data before it is used for training.
    Also, Data Engineer and Data Scientist would benefit from Data Lineage understanding through feature like Data Flow.
    I appreciate your feedbacks.

    1 vote
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  8. Please add support for federated learning in AML

    More and more users are opting not to send their data back to cloud. In order to respect user privacy and scale the training of our ML models, it would be great if AML would support federated learning.

    1 vote
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  9. 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…

    2 votes
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  10. Is it possible to change the web service URI from ".../score" to a custom name?

    I'm trying to replace an existing web service in Microsoft Dynamics that was using a model from Azure Machine Learning Studio. It has a hardcoded endpoint suffix like "/predict". For the Azure ML Service I'm building to replace it, there seems to be a hardcoded suffix called "/score" instead in the web service URI.

    Can I customize this? I'd like to replace "/score" with "/predict" etc so that I can swap out the new model.

    1 vote
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  11. Allow Properties Shared by Multiple Items to be Edited In a Single Place

    If I select several items in an Azure ML Studio experiement that all share a common property, I should be able to set that property in one place - once - and not have to edit each item separately.

    A huge portion of what data scientist are doing in an experiment is trying out different classification algorithms, training them, scoring them, and evaluating and comparing results. This makes for a lot of repetitive, monotonous work. And it means we have to do exactly the same edits (like setting a dependent variable) across a bunch of different items.

    There's no reason…

    3 votes
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    1 comment  ·  Flag idea as inappropriate…  ·  Admin →
  12. 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…

    1 vote
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    1 comment  ·  Flag idea as inappropriate…  ·  Admin →
  13. 2 votes
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  14. 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.

    1 vote
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    1 comment  ·  Flag idea as inappropriate…  ·  Admin →
  15. 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!

    1 vote
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    triaged  ·  1 comment  ·  Flag idea as inappropriate…  ·  Admin →
  16. MT-DNN-BERT Model inside Visual Interface of Azure ML Studio

    Any project need to use Semantic Models, badly need use either MT-DNN or BERT, can you please include MT-DNN or BERT Model inside Visual Interface of Azure ML Studio. Because that gives Azure edge over other AI Platforms DataRobot, other wise many customers move to other AI Platforms. I need to use either MT-DNN or BERT in my project. As per Gardner/Mckinsey report is saying more Intelligent Chat Bot with muliti lingual requirement flooding all over the world ever before in the AI.

    1 vote
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    1 comment  ·  Flag idea as inappropriate…  ·  Admin →
  17. Support Parquet file format as Import Data action

    Support for Parquet data format as Import Data, as many big data projects are using this format. It is much lighter then CSV. This format is already supported by Azure Data Factory.

    1 vote
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    0 comments  ·  Flag idea as inappropriate…  ·  Admin →

    Hi Azure customer,

    Thank you for the feedback here, could you please share more details about what product you are mentioning? Is that Azure machine learning studio, Visual Interface, or you mean for all products ^^?

    Waiting for your response. Thank you very much.

    Regards,
    Azure CXP Community

  18. Remove the limit on model names

    Currently the character limit on a model name is 30 characters. We need names to be as long as we want to satisfy our naming conventions.

    We have various sites our models run on, so we need to prefix the model name with the site, its operational area, it's purpose, and the algorithm choice.

    Surely there's no *hard* technical limitation on naming something longer, if so, enlighten your users as to why there is a limitation so that we are able to anticipate such things.

    When I check Azure naming Conventions (https://docs.microsoft.com/en-us/azure/architecture/best-practices/naming-conventions) the vast majority of the name…

    22 votes
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    0 comments  ·  Flag idea as inappropriate…  ·  Admin →

    Hi Customer,

    May I know which product of machine learning services you are mentioning? The visual interface, machine learning studio or machine learning workspace?

    We would like to specify the product and we can investigate it deeper.

    Regards,
    Azure CXP Community

  19. Please add API that can show that upper /lower Part separatley that "RunDetails().show()" draw.

    About using Auto ML with "RunDetails().show()" of "Azure Machine Learning Service Python SDK" ,
    Please add API that can show only that upper Table Part that "RunDetails().show()" draw.
    Also, Please add API that can show only that lower Chart part that "RunDetails().show()" draw.

    *please check attachment that show the upper table part and lower chart part.

    1 vote
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  20. More text processing options

    Provide more string processing options in ML Studio & Visual Interface for ML Service. I use python pandas df[col].str.functions like lower, upper, strip, etc. regularly. Also would be good to have extract and match. Doc is here https://pandas.pydata.org/pandas-docs/stable/user_guide/text.html

    1 vote
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