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. Data validation component in Azure Machine Learning designer

    A dataset validation component that is aimed at the Citizen Data Engineer as typically the data is pretty dynamic.

    37 votes
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  2. Need samples using AzureMLDataset in the designer

    It is very useful to have samples for Designer that uses AzureMLDataset (exported dataset of labeled data). There is much demand, so please consider it.

    40 votes
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  3. Enable Forecasting models when lagging features are enabled

    Currently, Forecasting models are disabled when user uses lagging features such as target lags and/or rolling windows. They should be added to the roadmap and made available even when lagging features are enabled.

    https://docs.microsoft.com/en-us/python/api/azureml-automl-core/azureml.automl.core.shared.constants.supportedmodels.forecasting?view=azure-ml-py
    -> 'AutoArima','Prophet','Average','Naive','SeasonalAverage','SeasonalNaive'

    13 votes
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  4. Add Object Detection Module in Azure ML Designer

    Though It is easy to create an image classification model in Azure ML Designer by using DenseNet or ResNet module, it's more convenient if some object detection modules can be provided in designer.

    13 votes
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  5. 14 votes
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  6. Ask for more unsupervised Models as Train Anomaly Detection Model in the Designer

    AML Studio (classic) supports One-Class SVM, but Designer does not. I would like Designer to support more unsupervised Models, such as One-Class SVM and Local Outlier Factor.

    36 votes
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  7. allow specifying the resource group of a virtual network when creating an container instance (endpoint) using the sdk (and portal).

    when running through the mnist tutorial with our setup i run into the following problem.

    aciconfig = AciWebservice.deployconfiguration(cpucores=1,

                                               memory_gb=1, 
    
    vnet_name="vnetname",
    subnet_name="subnetwithdelegation",
    tags={"data": "MNIST", "method" : "sklearn"},
    description='Predict MNIST with sklearn')

    there is no way to define the resource group of the virtual network in the deploy_configuration method and it defaults to the resource group of the machine learning services workspace. it would be nice to define the resource group name here so i can connect to a different vnet

    38 votes
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  8. Make AutoML a module in the Designer

    It would be very useful if AutoML could be used from within the Designer.

    3 votes
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  9. Avoiding LGPL license restriction in azureml-core phyton library

    Hello all,

    We are using azureml-core phyton library to interact with Azure Machine Learning Workspace. The library is hosted on https://pypi.org/project/azureml-core It is authored by Microsoft and maintained by pypi community. One of its dependencies of azureml-core is "chardet" phyton library and it has LGPL license restriction. https://pypi.org/project/chardet/

    Our compliance team would like us to avoid the use of LGPL licensed open source libraries to avoid future license implications. I couldn't find an alternative to azureml-core phyton library.

    I noticed that there are two GitHub initiatives to replace chardet . Please refer to below links:

    https://github.com/psf/requests/issues/4848

    https://github.com/encode/httpx/issues/1018

    I would like…

    18 votes
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  10. Automatic Shutdown for Compute VMs

    It would be great if we could have the ability to automatically shut down Compute VM's at a specified time - similar to what you can do with VM's provisioned outside of the AML Portal.

    24 votes
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  11. Create AML Datasets from within the Designer

    If the Designer could create fully functional AML Dataset (Tabular and/or File), it would be possble to have citizen data scientists work on data transformation, preparation and feature engineering and then use AutoML or the SDK to do actual Machine Learning with these Datasets.

    At the moment there is a disconnect between Designer and other authoring methods.

    1 vote
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  12. 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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  13. 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 vote
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    1 vote
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  16. 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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    1 vote
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  18. Monetization of pre-trained ML Models via AI-store

    As a user it would be great to be able to use an existing pre-trained model for some of my AI use-cases. This model can be supplied by a third party supplier. As a user I pay a fee per minute of usage.
    The same approach is currently available witin the AWS Sagemaker marketplace.

    3 votes
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  19. 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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    1 vote
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