Generative AI on AWS

Preface

After reading this book, you will understand the most common generative AI use cases and tasks addressed by industry and academia today. You will gain in-depth knowledge of how these cutting-edge generative models are built, as well as practical experience to help you choose between reusing an existing generative model or building one from scratch. You will then learn to adapt these generative AI models to your domain-specific datasets, tasks, and use cases that support your business applications.

This book is meant for AI/ML enthusiasts, data scientists, and engineers who want to learn the technical foundations and best practices for generative AI model training, fine-tuning, and deploying into production. We assume that you are already familiar with Python and basic deep-learning components like neural networks, forward propagation, activations, gradients, and back propagations to understand the concepts used here.

A basic understanding of Python and deep learning frameworks such as TensorFlow or PyTorch should be sufficient to understand the code samples used throughout the book. Familiarity with AWS is not required to learn the concepts, but it is useful for some of the AWS-specific samples.

You will dive deep into the generative AI life cycle and learn topics such as prompt engineering, few-shot in-context learning, generative model pretraining, domain adaptation, model evaluation, parameter-efficient fine-tuning (PEFT), and reinforcement learning from human feedback ...