Tutorial: Create your first classification model with automated machine learning
In this tutorial, you learn how to create your first automated machine learning experiment through the workspace landing page (preview) without writing a single line of code. This example creates a classification model to predict if a client will subscribe to a fixed term deposit with a financial institution.
With automated machine learning, you can automate away time intensive tasks. Automated machine learning rapidly iterates over many combinations of algorithms and hyperparameters to help you find the best model based on a success metric of your choosing.
In this tutorial, you learn how to do the following tasks:
- Create an Azure Machine Learning workspace.
- Run an automated machine learning experiment.
- View experiment details.
- Deploy the model.
An Azure subscription. If you don’t have an Azure subscription, create a free account.
Download the bankmarketing_train.csv data file. The y column indicates if a customer subscribed to a fixed term deposit, which is later identified as the target column for predictions in this tutorial.
Create a workspace
An Azure Machine Learning workspace is a foundational resource in the cloud that you use to experiment, train, and deploy machine learning models. It ties your Azure subscription and resource group to an easily consumed object in the service.
You create a workspace via the Azure portal, a web-based console for managing your Azure resources.
Sign in to the Azure portal by using the credentials for the Azure subscription you use.
In the upper-left corner of Azure portal, select + Create a resource.
Use the search bar to find Machine Learning service workspace.
Select Machine Learning service workspace.
In the Machine Learning service workspace pane, select Create to begin.
Provide the following information to configure your new workspace:
Field Description Workspace name Enter a unique name that identifies your workspace. In this example, we use docs-ws. Names must be unique across the resource group. Use a name that's easy to recall and to differentiate from workspaces created by others. Subscription Select the Azure subscription that you want to use. Resource group Use an existing resource group in your subscription or enter a name to create a new resource group. A resource group holds related resources for an Azure solution. In this example, we use docs-aml. Location Select the location closest to your users and the data resources to create your workspace.
After you are finished configuring the workspace, select Create.
It can take several minutes to create your workspace in the cloud.
When the process is finished, a deployment success message appears.
To view the new workspace, select Go to resource.
Take note of your workspace and subscription. You'll need these to ensure you create your experiment in the right place.
Create and run the experiment
You complete the following experiment set-up and run steps in the workspace landing page, a consolidated interface that includes machine learning tools to perform data science scenarios for data science practitioners of all skill levels. The workspace landing page is not supported on Internet Explorer browsers.
Sign in to the workspace landing page.
Select your subscription and the workspace you created.
Select Get started.
In the left pane, select Automated ML under the Author section.
Since this is your first automated ML experiment, you'll see the Getting started screen.
Select Create experiment.
Enter this experiment name:
Select Create a new compute and configure your compute target. A compute target is a local or cloud based resource environment used to run your training script or host your service deployment. For this experiment we use a cloud based compute.
Field Description Value for tutorial Compute name A unique name that identifies your compute context. automl-compute Virtual machine size Select the virtual machine size for your compute. Standard_DS12_V2 Min / Max nodes (in Advanced Settings) To profile data, you must specify 1 or more nodes. Min nodes: 1
Max nodes: 6
For this tutorial, you'll use the default storage account and container created with your new compute. They automatically populate in the form.
Select Create to get the compute target.
This takes a couple minutes to complete.
After creation, select your new compute target from the drop-down list and select Next.
Select Upload from local file to begin creating a new dataset.
Choose the bankmarketing_train.csv file on your local computer. This is the file you downloaded as a prerequisite.
Give your dataset a unique name and provide an optional description.
Select Next on the bottom left, to upload it to the default container that was automatically set up during your workspace creation. Public preview supports only local file uploads.
When the upload is complete, the Settings and preview form is pre-populated based on the file type.
Verify that the Settings and preview form is populated as follows and select Next.
Field Description Value for tutorial File format Defines the layout and type of data stored in a file. Delimited Delimiter One or more characters for specifying the boundary between separate, independent regions in plain text or other data streams. Comma Encoding Identifies what bit to character schema table to use to read your dataset. UTF-8 Column headers Indicates how the headers of the dataset, if any, will be treated. All files have same headers Skip rows Indicates how many, if any, rows are skipped in the dataset. None
Select Classification as the prediction task.
Select y as the target column, what you want to predict. This column indicates whether the client subscribed to a term deposit or not.
Expand Advanced Settings and populate the fields as follows. These settings are to better control the training job. Otherwise, defaults are applied based on experiment selection and data.
In this tutorial, you won't set a metric score or max cores per iterations threshold. Nor will you block algorithms from being tested.
Advanced settings Description Value for tutorial Primary metric Evaluation metric that the machine learning algorithm will be measured by. AUC_weighted Exit criteria If a criteria is met, the training job is stopped. Training job time: 5
Max # of iterations:10
Preprocessing Enables preprocessing done by automated machine learning. This includes automatic data cleansing, preparing, and transformation to generate synthetic features. Enable Validation type Choose a cross-validation type. K-fold cross-validation Number of validations Number of tests. 2 cross-validations Concurrency The number of max concurrent iterations. 5
Select Start to run the experiment. A screen appears with a status message as the experiment preparation begins.
Preparation takes 10-15 minutes to prepare the experiment run. Once running, it takes 2-3 minutes more for each iteration.
In production, you'd likely walk away for a bit. But for this tutorial, we suggest you start exploring the iteration results as they complete while the others are still running.
Explore iteration results
As the experiment progresses, the screen updates the Iteration chart and Iteration list with the different iterations (models) created as they complete. By default, the iterations are ordered by metric score. For this tutorial, the model that scores the highest based on the chosen AUC_weighted metric is at the top of the list.
While you wait for all of the experiment iterations to finish, select the Name of a completed iteration to explore its performance details.
The following shows the charts and run metrics generated for each iteration such as, a precision-recall curve, confusion matrix, weighted accuracy scores, etc.
Deploy the model
Automated machine learning in the workspace landing page allows you to deploy the best model as a web service in a few steps. Deployment is the integration of the model so it can predict on new data and identify potential areas of opportunity. For this experiment, deployment to a web service means that the financial institution now has an iterative and scalable web solution for identifying potential fixed term deposit customers.
Once the run is complete, navigate back to the Iteration chart and Iterations list detail page.
In this experiment context, VotingEnsemble is considered the best model, based on the AUC_weighted metric. We deploy this model, but be advised, deployment takes about 20 minutes to complete. The deployment process entails several steps including registering the model, generating resources, and configuring them for the web service.
Select the Deploy Best Model button in the top-right corner.
Populate the Deploy Best Model pane as follows:
Field Value Deployment name my-automl-deploy Deployment description My first automated machine learning experiment deployment Scoring script Autogenerate Environment script Autogenerate
A deployment complete message appears when deployment successfully finishes.
Now you have an operational web service to generate predictions.
Clean up resources
Deployment files are larger than data and experiment files, so they cost more to store. Delete only the deployment files to minimize costs to your account, or if you want to keep your workspace and experiment files. Otherwise, delete the entire resource group, if you don't plan to use any of the files.
Delete the deployment instance
Delete just the deployment instance from the Azure portal, if you want to keep the resource group and workspace for other tutorials and exploration.
Go to the Azure portal. Navigate to your workspace and on the left under the Assets pane, select Deployments.
Select the deployment you want to delete and select Delete.
Delete the resource group
The resources you created can be used as prerequisites to other Azure Machine Learning service tutorials and how-to articles.
If you don't plan to use the resources you created, delete them, so you don't incur any charges:
In the Azure portal, select Resource groups on the far left.
From the list, select the resource group you created.
Select Delete resource group.
Enter the resource group name. Then select Delete.
In this automated machine learning tutorial, you used the workspace landing page to create and deploy a classification model. See these articles for more information and next steps:
- Learn more about preprocessing.
- Learn more about data profiling.
- Learn more about automated machine learning.
- For more information on classification metrics and charts see the Understand automated machine learning results article.
This Bank Marketing dataset is made available under the Creative Commons (CCO: Public Domain) License. Any rights in individual contents of the database are licensed under the Database Contents License and available on Kaggle. This dataset was originally available within the UCI Machine Learning Database.
[Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014.