1. Building an end-to-end machine learning pipeline in Azure

This first chapter covers all the required components for running a custom end-to-end machine learning (ML) pipeline in Azure. Some sections might be a recap of your existing knowledge with useful practical tips, step-by-step guidelines, and pointers to using Azure services to perform ML at scale. You can see it as an overview of the book, where we will dive into each section in great detail with many practical examples and a lot of code during the remaining chapters of the book.

First, we will look at data experimentation techniques as a step-by-step process for analyzing common insights, such as missing values, data distribution, feature importance, and two-dimensional embedding techniques to estimate the expected model performance of a classification task. In the second section, we will use these insights about the data to perform data preprocessing and feature engineering, such as normalization, the encoding...