Designing Machine Learning Systems

Chapter 2. Introduction to Machine Learning Systems Design

Now that we’ve walked through an overview of ML systems in the real world, we can get to the fun part of actually designing an ML system. To reiterate from the first chapter, ML systems design takes a system approach to MLOps, which means that we’ll consider an ML system holistically to ensure that all the components—the business requirements, the data stack, infrastructure, deployment, monitoring, etc.—and their stakeholders can work together to satisfy the specified objectives and requirements.

We’ll start the chapter with a discussion on objectives. Before we develop an ML system, we must understand why this system is needed. If this system is built for a business, it must be driven by business objectives, which will need to be translated into ML objectives to guide the development of ML models.

Once everyone is on board with the objectives for our ML system, we’ll need to set out some requirements to guide the development of this system. In this book, we’ll consider the four requirements: reliability, scalability, maintainability, and adaptability. We will then introduce the iterative process for designing systems to meet those requirements.

You might wonder: with all these objectives, requirements, and processes in place, can I finally start building my ML model yet? Not so soon! Before using ML algorithms to solve your problem, you first need to frame your problem into a task that ML can solve. We’ll continue this ...