Summary

In this chapter, you learned how to configure an AutoML process to discover the best model that can predict whether a customer will churn or not. First, you used the AutoML wizard of the Azure Machine Learning Studio web experience to configure the experiment. Then, you monitored the execution of the run in the Experiments section of the studio interface. Once the training was completed, you reviewed the trained models and saw the information that had been stored regarding the best model. Then, you deployed that machine learning model in an Azure Container Instance and tested that the real-time endpoint performs the requested inferences. In the end, you deleted the deployment to avoid incurring costs in your Azure subscription.

In the next chapter, you will continue exploring the no-code/low code aspects of the Azure Machine Learning Studio experience by looking at the designer, which allows you to graphically design a training pipeline and operationalize the produced model...