Generative AI on Aws

Companies today are moving rapidly to integrate generative AI into their products and services. But there's a great deal of hype (and misunderstanding) about the impact and promise of this technology. In this book, Chris Fregly, Antje Barth, and Shelbee Eigenbrode from AWS help CTOs, machine learning practitioners, business analysts, data engineers, and data scientists find a practical way to use this exciting new technology. You'll learn the generative AI project lifecycle including use case definition, model selection, model fine-tuning, retrieval-augmented generation (RAG), reinforcement learning from human feedback (RLHF), model quantization, optimization, and deployment. You'll explore different types of models including large language models (LLMs) and multimodal models such as Stable Diffusion for generating images and video. You'll also be able to make better-informed decisions for your company regarding generative AI and learn how to build working prototypes quickly. While the focus is on AWS, this book is a great resource for learning generative AI fundamentals and applying these models to real-world applications. Apply generative AI to your business use cases Determine which generative AI models to use based on the task Perform prompt engineering and in-context learning Fine-tune generative AI models on your datasets Align generative AI models to human values with reinforcement learning from human feedback Use techniques like retrieval-augmented generation to augment your model Explore libraries such as LangChain and React to develop agents and actions Learn about multimodal models such as Stable Diffusion for image and video generation Get hands-on with Amazon Bedrock, the AWS generative AI managed service

Chris Fregly is a Principal Solutions Architect for Generative AI at Amazon Web Services (AWS) based in San Francisco, California. Chris holds every AWS certification. He is also co-founder of the global Generative AI on AWS Meetup. Chris regularly speaks at AI and Machine Learning meetups and conferences across the world. Previously, Chris was an engineer at Databricks and Netflix where he worked on scalable big data and machine learning products and solutions. He is also co-author of the O'Reilly book, Data Science on AWS. Antje Barth is a Principal Developer Advocate for AI and Machine Learning at Amazon Web Services (AWS) based in San Francisco, California. She is also co-founder of the global Generative AI on AWS Meetup. Antje frequently speaks at AI and machine learning conferences and meetups around the world, including the O'Reilly AI and Strata conferences. Besides Generative AI, Antje is passionate about helping developers leverage big data, containers, and Kubernetes platforms in the context of AI and Machine Learning. Prior to joining AWS, Antje worked in technical evangelist and solutions engineering roles at MapR and Cisco. She is also co-author of the O'Reilly book, Data Science on AWS. Shelbee Eigenbrode is a Principal Solutions Architect for Generative AI at Amazon Web Services (AWS) based in Denver, Colorado. She is co-founder of the Denver chapter of Women in Big Data. Shelbee holds 6 AWS certifications and has been in technology for 23 years spanning multiple industries, technologies, and roles. She focuses on combining her DevOps and ML backgrounds to deliver ML workloads at scale. With over 35 patents granted across various technology domains, Shelbee has a passion for continuous innovation and using data to drive business outcomes.