Set up no-code AutoML training with the studio UI

In this article, you learn how to set up AutoML training runs without a single line of code using Azure Machine Learning automated ML in the Azure Machine Learning studio.

Automated machine learning, AutoML, is a process in which the best machine learning algorithm to use for your specific data is selected for you. This process enables you to generate machine learning models quickly. Learn more about how Azure Machine Learning implements automated machine learning.

For an end to end example, try the Tutorial: AutoML- train no-code classification models.

For a Python code-based experience, configure your automated machine learning experiments with the Azure Machine Learning SDK.


Get started

  1. Sign in to Azure Machine Learning studio.

  2. Select your subscription and workspace.

  3. Navigate to the left pane. Select Automated ML under the Author section.

Azure Machine Learning studio navigation pane

If this is your first time doing any experiments, you'll see an empty list and links to documentation.

Otherwise, you'll see a list of your recent automated ML experiments, including those created with the SDK.

Create and run experiment

  1. Select + New automated ML run and populate the form.

  2. Select a dataset from your storage container, or create a new dataset. Datasets can be created from local files, web urls, datastores, or Azure open datasets. Learn more about dataset creation.


    Requirements for training data:

    • Data must be in tabular form.
    • The value you want to predict (target column) must be present in the data.
    1. To create a new dataset from a file on your local computer, select +Create dataset and then select From local file.

    2. In the Basic info form, give your dataset a unique name and provide an optional description.

    3. Select Next to open the Datastore and file selection form. On this form you select where to upload your dataset; the default storage container that's automatically created with your workspace, or choose a storage container that you want to use for the experiment.

      1. If your data is behind a virtual network, you need to enable the skip the validation function to ensure that the workspace can access your data. For more information, see Use Azure Machine Learning studio in an Azure virtual network.
    4. Select Browse to upload the data file for your dataset.

    5. Review the Settings and preview form for accuracy. The form is intelligently populated based on the file type.

      Field Description
      File format Defines the layout and type of data stored in a file.
      Delimiter One or more characters for specifying the boundary between separate, independent regions in plain text or other data streams.
      Encoding Identifies what bit to character schema table to use to read your dataset.
      Column headers Indicates how the headers of the dataset, if any, will be treated.
      Skip rows Indicates how many, if any, rows are skipped in the dataset.

      Select Next.

    6. The Schema form is intelligently populated based on the selections in the Settings and preview form. Here configure the data type for each column, review the column names, and select which columns to Not include for your experiment.

      Select Next.

    7. The Confirm details form is a summary of the information previously populated in the Basic info and Settings and preview forms. You also have the option to create a data profile for your dataset using a profiling enabled compute. Learn more about data profiling.

      Select Next.

  3. Select your newly created dataset once it appears. You are also able to view a preview of the dataset and sample statistics.

  4. On the Configure run form, select Create new and enter Tutorial-automl-deploy for the experiment name.

  5. Select a target column; this is the column that you would like to do predictions on.

  6. Select a compute for the data profiling and training job. A list of your existing computes is available in the dropdown. To create a new compute, follow the instructions in step 7.

  7. Select Create a new compute to configure your compute context for this experiment.

    Field Description
    Compute name Enter a unique name that identifies your compute context.
    Virtual machine priority Low priority virtual machines are cheaper but don't guarantee the compute nodes.
    Virtual machine type Select CPU or GPU for virtual machine type.
    Virtual machine size Select the virtual machine size for your compute.
    Min / Max nodes To profile data, you must specify 1 or more nodes. Enter the maximum number of nodes for your compute. The default is 6 nodes for an AML Compute.
    Advanced settings These settings allow you to configure a user account and existing virtual network for your experiment.

    Select Create. Creation of a new compute can take a few minutes.


    Your compute name will indicate if the compute you select/create is profiling enabled. (See the section data profiling for more details).

    Select Next.

  8. On the Task type and settings form, select the task type: classification, regression, or forecasting. See supported task types for more information.

    1. For classification, you can also enable deep learning.

      If deep learning is enabled, validation is limited to train_validation split. Learn more about validation options.

    2. For forecasting you can,

      1. Enable deep learning.

      2. Select time column: This column contains the time data to be used.

      3. Select forecast horizon: Indicate how many time units (minutes/hours/days/weeks/months/years) will the model be able to predict to the future. The further the model is required to predict into the future, the less accurate it will become. Learn more about forecasting and forecast horizon.

  9. (Optional) View addition configuration settings: additional settings you can use to better control the training job. Otherwise, defaults are applied based on experiment selection and data.

    Additional configurations Description
    Primary metric Main metric used for scoring your model. Learn more about model metrics.
    Explain best model Select to enable or disable, in order to show explanations for the recommended best model.
    This functionality is not currently available for certain forecasting algorithms.
    Blocked algorithm Select algorithms you want to exclude from the training job.

    Allowing algorithms is only available for SDK experiments.
    See the supported models for each task type.
    Exit criterion When any of these criteria are met, the training job is stopped.
    Training job time (hours): How long to allow the training job to run.
    Metric score threshold: Minimum metric score for all pipelines. This ensures that if you have a defined target metric you want to reach, you do not spend more time on the training job than necessary.
    Validation Select one of the cross validation options to use in the training job.
    Learn more about cross validation.

    Forecasting only supports k-fold cross validation.
    Concurrency Max concurrent iterations: Maximum number of pipelines (iterations) to test in the training job. The job will not run more than the specified number of iterations. Learn more about how automated ML performs multiple child runs on clusters.
  10. (Optional) View featurization settings: if you choose to enable Automatic featurization in the Additional configuration settings form, default featurization techniques are applied. In the View featurization settings you can change these defaults and customize accordingly. Learn how to customize featurizations.

    Screenshot shows the Select task type dialog box with View featurization settings called out.

Customize featurization

In the Featurization form, you can enable/disable automatic featurization and customize the automatic featurization settings for your experiment. To open this form, see step 10 in the Create and run experiment section.

The following table summarizes the customizations currently available via the studio.

Column Customization
Included Specifies which columns to include for training.
Feature type Change the value type for the selected column.
Impute with Select what value to impute missing values with in your data.

Azure Machine Learning studio custom featurization

Run experiment and view results

Select Finish to run your experiment. The experiment preparing process can take up to 10 minutes. Training jobs can take an additional 2-3 minutes more for each pipeline to finish running.


The algorithms automated ML employs have inherent randomness that can cause slight variation in a recommended model's final metrics score, like accuracy. Automated ML also performs operations on data such as train-test split, train-validation split or cross-validation when necessary. So if you run an experiment with the same configuration settings and primary metric multiple times, you'll likely see variation in each experiments final metrics score due to these factors.

View experiment details

The Run Detail screen opens to the Details tab. This screen shows you a summary of the experiment run including a status bar at the top next to the run number.

The Models tab contains a list of the models created ordered by the metric score. By default, the model that scores the highest based on the chosen metric is at the top of the list. As the training job tries out more models, they are added to the list. Use this to get a quick comparison of the metrics for the models produced so far.

Run detail

View training run details

Drill down on any of the completed models to see training run details. On the Model tab view details like a model summary and the hyperparameters used for the selected model.

Hyperparameter details

You can also see model specific performance metric charts on the Metrics tab. Learn more about charts.

Iteration details

On the Data transformation tab, you can see a diagram of what data preprocessing, feature engineering, scaling techniques and the machine learning algorithm that were applied to generate this model.


The Data transformation tab is in preview. This capability should be considered experimental and may change at any time.

Data transformation

Model explanations (preview)

To better understand your model, you can see which data features (raw or engineered) influenced the model's predictions with the model explanations dashboard.

The model explanations dashboard provides an overall analysis of the trained model along with its predictions and explanations. It also lets you drill into an individual data point and its individual feature importances. Learn more about the explanation dashboard visualizations.

To get explanations for a particular model,

  1. On the Models tab, select the model you want to understand.

  2. Select the Explain model button, and provide a compute that can be used to generate the explanations.

  3. Check the Child runs tab for the status.

  4. Once complete, navigate to the Explanations (preview) tab which contains the explanations dashboard.

    Model explanation dashboard

Deploy your model

Once you have the best model at hand, it is time to deploy it as a web service to predict on new data.


If you are looking to deploy a model that was generated via the automl package with the Python SDK, you must register your model to the workspace.

Once you're model is registered, find it in the studio by selecting Models on the left pane. Once you open your model, you can select the Deploy button at the top of the screen, and then follow the instructions as described in step 2 of the Deploy your model section.

Automated ML helps you with deploying the model without writing code:

  1. You have a couple options for deployment.

    • Option 1: Deploy the best model, according to the metric criteria you defined.

      1. After the experiment is complete, navigate to the parent run page by selecting Run 1 at the top of the screen.
      2. Select the model listed in the Best model summary section.
      3. Select Deploy on the top left of the window.
    • Option 2: To deploy a specific model iteration from this experiment.

      1. Select the desired model from the Models tab
      2. Select Deploy on the top left of the window.
  2. Populate the Deploy model pane.

    Field Value
    Name Enter a unique name for your deployment.
    Description Enter a description to better identify what this deployment is for.
    Compute type Select the type of endpoint you want to deploy: Azure Kubernetes Service (AKS) or Azure Container Instance (ACI).
    Compute name Applies to AKS only: Select the name of the AKS cluster you wish to deploy to.
    Enable authentication Select to allow for token-based or key-based authentication.
    Use custom deployment assets Enable this feature if you want to upload your own scoring script and environment file. Learn more about scoring scripts.


    File names must be under 32 characters and must begin and end with alphanumerics. May include dashes, underscores, dots, and alphanumerics between. Spaces are not allowed.

    The Advanced menu offers default deployment features such as data collection and resource utilization settings. If you wish to override these defaults do so in this menu.

  3. Select Deploy. Deployment can take about 20 minutes to complete. Once deployment begins, the Model summary tab appears. See the deployment progress under the Deploy status section.

Now you have an operational web service to generate predictions! You can test the predictions by querying the service from Power BI's built in Azure Machine Learning support.

Next steps