Creating an ML pipeline
ML pipelines are Azure's solution for batch scoring ML models. You can use ML pipelines to score any model you train, including your own custom models as well as AutoML-generated models. They can only be created via code using the Azure ML Python SDK. In this section, you will code a simple pipeline to score diabetes data using the Diabetes-AllData-Regression-AutoML model you built in Chapter 4, Building an AutoML Regression Solution.
As in other chapters, you will begin by opening your compute instance and navigating to your Jupyter notebook environment. You will then create and name a new notebook. Once your notebook is created, you will build, configure, and run an ML pipeline step by step. After confirming your pipeline has run successfully, you will then publish your ML pipeline to a pipeline endpoint. Pipeline endpoints are simply URLs, web addresses that call ML pipeline runs.
The following steps deviate greatly from previous chapters. You...
