Apply creativity and engineering to create apps with transformer and diffusion based large and foundational models Purchase of the print or Kindle book includes a free PDF eBook Key Features Learn with practical examples, code snippets, and use cases from a variety of generative AI applications Conquer the core concepts and techniques of generative AI engineering Get to grips with productionizing operational pipelines with generative AI Book DescriptionGenerative AI Engineering is a hands-on guide to utilizing generative AI for creating advanced AI based applications. It’s designed for both beginners and experienced practitioners who want to learn how to develop generative AI models-based applications and use them to solve real-world problems. As you progress through the chapters, you’ll cover all the core concepts and techniques of generative AI engineering, including transformer and diffusion-based models. You'll also learn from practical examples and carefully chosen code snippets that will help you understand how to apply these concepts to real-world problems. You will learn the generative AI services and platforms offered by major cloud providers, GCP, AWS, and Microsoft Azure. With its comprehensive coverage and practical examples, this book will provide you with the skills and knowledge you need to build cutting-edge AI applications that can create new content, generate realistic images and videos, and solve complex problems in a wide range of industries.What you will learn Get to grips with the fundamentals of generative AI and its applications Familiarize yourself with different types of generative models and when to use them Train and Finetune generative models using PyTorch Evaluate the performance of your models and fine-tune them for optimal results Find best practices for deploying and scaling generative AI models in production environments Who this book is forWhether you are a software engineer, data scientist, or AI enthusiast, Generative AI Engineering is an essential resource that will help you master the art of building generative AI models. To get started, you’ll need to have basic knowledge of Python coding, including Jupyter Notebooks, setup cloud or local infrastructure using Docker containers for sample code, and deep learning and PyTorch.