Chapter 8: Azure Machine Learning Pipelines
In the previous chapter, we learned about advanced preprocessing techniques, such as category embeddings and NLP, to extract semantic meaning from text features. In this chapter, you will learn how to use these preprocessing and transformation techniques to build reusable ML pipelines.
First, you will understand the benefits of splitting your code into individual steps and wrapping those into a pipeline. Not only can you make your code blocks reusable through modularization and parameters, but you can also control the compute targets for individual steps. This helps to optimally scale your computations, save costs, and improve performance at the same time. Lastly, you can parameterize and trigger your pipelines through an HTTP endpoint or through a recurring or reactive schedule.
Then, we will build a complex Azure Machine Learning pipeline in a couple of steps. We will start with a simple pipeline, add data inputs, outputs, and connections...