Chapter 1: Introduction to ML Engineering
Welcome to Machine Learning Engineering with Python, a book that aims to introduce you to the exciting world of making Machine Learning (ML) systems production-ready.
This book will take you through a series of chapters covering training systems, scaling up solutions, system design, model tracking, and a host of other topics, to prepare you for your own work in ML engineering or to work with others in this space. No book can be exhaustive on this topic, so this one will focus on concepts and examples that I think cover the foundational principles of this increasingly important discipline.
You will get a lot from this book even if you do not run the technical examples, or even if you try to apply the main points in other programming languages or with different tools. In covering the key principles, the aim is that you come away from this book feeling more confident in tackling your own ML engineering challenges, whatever your chosen toolset.
In this first chapter, you will learn about the different types of data role relevant to ML engineering and how to distinguish them; how to use this knowledge to build and work within appropriate teams; some of the key points to remember when building working ML products in the real world; how to start to isolate appropriate problems for engineered ML solutions; and how to create your own high-level ML system designs for a variety of typical business problems.
We will cover all of these aspects in the following sections:
- Defining a taxonomy of data disciplines
- Assembling your team
- ML engineering in the real world
- What does an ML solution look like?
- High-level ML system design
Now that we have explained what we are going after in this first chapter, let's get started!
