Understanding AzureML pipelines
In Chapter 6, Visual Model Training and Publishing, you saw how you can design a training process using building boxes. Similar to those workflows, the AzureML SDK allows you to author Pipelines that orchestrate multiple steps. For example, in this chapter, you will author a Pipeline that consists of two steps. The first step pre-processes the loans dataset that is regarded as raw training data and stores it in a temporary location. The second step then reads this data and trains a machine learning model, which will be stored in a blob store location. In this example, each step will be nothing more than a Python script file that is being executed in a specific compute target using a predefined Environment.
Important note
Do not confuse the AzureML Pipelines with the sklearn Pipelines you read in Chapter 10, Understanding Model Results. The sklearn ones allow you to chain various transformations and feature engineering methods to transform the data...
