Azure Machine Learning Studio

By Ashwin Venugopal -

 





Create and Train a Model in Azure Machine Learning Studio

With Azure AI Machine Learning Studio, you can utilize a designer to develop pipelines that generate and train models using drag and drop. Using prebuilt templates is an even simpler method of model creation.

In order to deploy your models to a web service, they must be registered in Azure AI Machine Learning Studio, regardless of how you choose to construct them.

Alter How the Model Works to Allow it to be Called by the AML custom skill

Typically, a large number of data instances are used to train the models. To train and test the model, the datasets will be divided into many rows. To handle single rows, the code that processes this input and sends it to the model must be modified.

Only the output prediction should be included in the model's JSON response.

Create an Endpoint For Your Model to Use

An endpoint receives the model's deployment. A model can be deployed to a web service, a batch endpoint, or a real-time endpoint using Azure AI Machine Learning Studio. At the moment, the custom AmlSkill skill in Azure AI Search only supports web service endpoints.

The endpoint must be an Azure Kubernetes Service (AKS) cluster, which is another limitation. Instances of containers are not supported. If you have experience in creating and managing AKS clusters, you can manually create the clusters in the Azure portal and reference them when you create your endpoint. However, an easier option is to let Azure AI Machine Learning Studio create and manage the cluster for you.

You can create inference clusters by going to the studio's compute area. After that, AML Studio will let you select the cluster's size, enable HTTPS, and generate a domain name. It will be in the format of location.cloudapp.azure.com:443 .

Connect the AML Custom Skill To the Endpoint

With everything above in place, you need to update your Azure AI Search service. The following are a few steps to follow:

  • First, to enrich your search index you'll add a new field to your index to include the output for the model.

  • Then you will update your index skillset and add the #Microsoft.Skills.Custom.AmlSkill custom skill.

  • Next, you will change your indexer to map the output from the custom skill to the field you created on the index.

  • The last step is to rerun your indexer to enrich your index with the AML model.

Conclusion

We have successfully learnt about creating and training a model in Azure Machine Learning Studio.

 






















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