Part 1 Basic concepts and background

This part of the book will provide some background and concepts related to distributed machine learning systems. We will start by discussing the growing scale of machine learning applications (given users’ demand for faster responses to meet real-life requirements), machine learning pipelines, and model architectures. Then we will talk about what a distributed system is, describe its complexity, and introduce one concrete example pattern that’s often used in distributed systems.

In addition, we will discuss what distributed machine learning systems are, examine similar patterns that are often used in those systems, and talk about some real-life scenarios. At the end of this part, we will take a glance at what we’ll be learning in this book.