Building Intelligent Cloud Applications

Chapter 1. Machine Learning and Deep Learning Models in the Cloud

It doesn’t seem that long ago that artificial intelligence (AI) was a dream. The idea that a machine could simulate and even beat humans at games of skill, image recognition, and predictions was preposterous 20 years ago. Now, the average user brushes up against some form of machine learning every day and everywhere—from our cars to stores to doctors’ offices, and throughout our homes.

We are living at the dawn of thinking machines. But how do they think? What do they use to build models of the world? And how can we, as developers, use these tools to make our systems smarter, more responsive, and more lifelike?

Our goal in this book is to discuss the basics of machine learning and to show you, in a step-by-step introduction, how to implement and code machine learning into your projects using serverless systems and pretrained models. We can think of machine learning as a tool for interacting with an ever-changing world using models that change and grow as they experience more of that world. In other words, it’s how we teach computers new things without explicitly programming them to do anything.

An Introduction to Machine Learning

The AI discipline, of which machine learning is a part, was born in the 1950s, during the Cold War, as a promise to develop systems that could solve complex problems. At that time, computers were not powerful enough for the task. Over the years, AI began to encompasses many different subdisciplines, ...