Preface
Almost every company nowadays is either using or trying to use AI/ML in some way, especially with the recent revolutions regarding generative AI. While AI/ML research is undoubtedly complex, what is often more complex is actually building and running applications that use AI/ML effectively. This book teaches you how to successfully design and run AI/ML workloads, based on years of experience implementing large-scale and highly complex AI/ML projects at some of the world’s leading technology companies.
The early chapters in this book provide an overview of the different categories of artificial intelligence and machine learning (AI/ML), as well as general cloud computing concepts. This is followed by an overview of Google Cloud, including the Google Cloud products related to AI/ML and examples of their intended use cases.
Then, the book progresses through the stages of a typical machine-learning project and model development life-cycle. Each chapter covers an important stage in the life-cycle. You will not only learn those concepts but will put them into action in the practical exercises that accompany each chapter. The process begins with procuring and preparing data and moves on to training ML models. Then, we will deploy the models and get inferences from them. You will also learn about monitoring and updating models after deployment to ensure that they continue to provide the best possible results. Additionally, you will automate all of those steps by building an end-to-end MLOps solution.
The book not only covers all of the steps in the machine-learning model development life-cycle but also covers important topics in implementing and managing machine-learning solutions at enterprise scale. We will dive into considerations such as privacy, compliance, ethics, and many other topics that are necessary to understand for running ML solutions in a real business context.
By the end of this book, you will possess advanced knowledge of cloud computing, Google Cloud, AI/ML, and generative AI. You will have built complex projects, solutions, and models, addressing real-world business use cases, and have learned common challenges that companies often run into when building AI/ML solutions, as well as how to address those challenges, based on many years of experience on some of the industry’s largest and most complex AI/ML systems and projects. You will also have learned and implemented important solution architecture considerations such as reliability, scalability, and security, and how they apply to AI/ML use cases.
These are among the most in-demand and high-paying skills in the technology industry and among the most sought-after skills in the world, in general, across all industries. With that in mind, join me on this journey and begin advancing your career today.
