Chapter 1: Understanding the End-to-End Machine Learning Process
Welcome to the second edition of Mastering Azure Machine Learning. In this first chapter, we want to give you an understanding of what kinds of problems require the use of machine learning (ML), how the full ML process unfolds, and what knowledge is required to navigate this vast terrain. You can view it as an introduction to ML and an overview of the book itself, where for most topics we will provide you with a reference to upcoming chapters so that you can easily find your way around the book.
In the first section, we will ask ourselves what ML is, when we should use it, and where it comes from. In addition, we will reflect on how ML is just another form of programming.
In the second section, we will lay the mathematical groundwork you require to process data, and we will understand that the data you work with probably cannot be fully trusted. Further, we will look at different classes of ML algorithms, how they are defined, and how we can define the performance of a trained model.
Finally, in the third section, we will have a look at the end-to-end process of an ML project. We will understand where to get data from, how to preprocess data, how to choose a fitting model, and how to deploy this model into production environments. This will also get us into the topic of ML operations, known as MLOps.
In this chapter, we will cover the following topics:
- Grasping the idea behind ML
- Understanding the mathematical basis for statistical analysis and ML modeling
- Discovering the end-to-end ML process