Designing Machine Learning Systems

Chapter 1. Overview of Machine Learning Systems

In November 2016, Google announced that it had incorporated its multilingual neural machine translation system into Google Translate, marking one of the first success stories of deep artificial neural networks in production at scale.¹ According to Google, with this update, the quality of translation improved more in a single leap than they had seen in the previous 10 years combined.

This success of deep learning renewed the interest in machine learning (ML) at large. Since then, more and more companies have turned toward ML for solutions to their most challenging problems. In just five years, ML has found its way into almost every aspect of our lives: how we access information, how we communicate, how we work, how we find love. The spread of ML has been so rapid that it’s already hard to imagine life without it. Yet there are still many more use cases for ML waiting to be explored in fields such as health care, transportation, farming, and even in helping us understand the universe.²

Many people, when they hear “machine learning system,” think of just the ML algorithms being used such as logistic regression or different types of neural networks. However, the algorithm is only a small part of an ML system in production. The system also includes the business requirements that gave birth to the ML project in the first place, the interface where users and developers interact with your system, the data stack, and the logic for developing, ...