Preface

Machine learning has gained a lot of popularity in recent years. The introduction of large language models such as GPT-3 and 4 only increased the speed of the development of this field. These large language models have become so powerful that it is almost impossible to train them on a local computer. However, this is not necessary at all. These language models provide the ability to create new tools without the need to train them because they can be steered by the context window and the prompt.

In this book, my goal is to show how machine learning models can be trained, evaluated, and tested – both in the context of a small prototype and in the context of a fully-fledged software product. The primary objective of this book is to bridge the gap between theoretical knowledge and practical implementation of machine learning in software engineering. It aims to equip you with the skills necessary to not only understand but also effectively implement and innovate with AI and machine learning technologies in your professional pursuits.

The journey of integrating machine learning into software engineering is as thrilling as it is challenging. As we delve into the intricacies of machine learning infrastructure, this book serves as a comprehensive guide, navigating through the complexities and best practices that are pivotal for software engineers. It is designed to bridge the gap between the theoretical aspects of machine learning and the practical challenges faced during implementation in real-world scenarios.

We begin by exploring the fundamental concepts of machine learning, providing a solid foundation for those new to the field. As we progress, the focus shifts to the infrastructure – the backbone of any successful machine learning project. From data collection and processing to model training and deployment, each step is crucial and requires careful consideration and planning.

A significant portion of the book is dedicated to best practices. These practices are not just theoretical guidelines but are derived from real-life experiences and case studies that my research team discovered during our work in this field. These best practices offer invaluable insights into handling common pitfalls and ensuring the scalability, reliability, and efficiency of machine learning systems.

Furthermore, we delve into the ethics of data and machine learning algorithms. We explore the theories behind ethics in machine learning, look closer into the licensing of data and models, and finally, explore the practical frameworks that can quantify bias in data and models in machine learning.

This book is not just a technical guide; it is a journey through the evolving landscape of machine learning in software engineering. Whether you are a novice eager to learn, or a seasoned professional seeking to enhance your skills, this book aims to be a valuable resource, providing clarity and direction in the exciting and ever-changing world of machine learning.