Google Machine Learning and Generative AI for Solutions Architects - Kavanagh, Kieran
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Architect and run real-world AI/ML solutions at scale on Google Cloud, and discover best practices to address common industry challenges effectively Key Features: - Understand key concepts, from fundamentals through to complex topics, via a methodical approach - Build real-world end-to-end MLOps solutions and generative AI applications on Google Cloud - Get your hands on a code repository with over 20 hands-on projects for all stages of the ML model development lifecycle - Purchase of the print or Kindle book includes a free PDF eBook Book Description: Nearly all companies nowadays either…mehr

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Produktbeschreibung
Architect and run real-world AI/ML solutions at scale on Google Cloud, and discover best practices to address common industry challenges effectively Key Features: - Understand key concepts, from fundamentals through to complex topics, via a methodical approach - Build real-world end-to-end MLOps solutions and generative AI applications on Google Cloud - Get your hands on a code repository with over 20 hands-on projects for all stages of the ML model development lifecycle - Purchase of the print or Kindle book includes a free PDF eBook Book Description: Nearly all companies nowadays either already use or are trying to incorporate AI/ML into their businesses. While AI/ML research is undoubtedly complex, the building and running of apps that utilize AI/ML effectively is tougher. This book shows you exactly how to design and run AI/ML workloads successfully using years of experience some of the world's leading tech companies have to offer. You'll begin by gaining a clear understanding of essential fundamental AI/ML concepts, before moving on to grasp complex topics with the help of examples and hands-on activities. This will help you eventually explore advanced, cutting-edge AI/ML applications that address real-world use cases in today's market. As you advance, you'll recognize the common challenges that companies face when implementing AI/ML workloads, and discover industry-proven best practices to overcome these challenges. The chapters also teach you about the vast AI/ML landscape on Google Cloud and how to implement all the steps needed in a typical AI/ML project. You'll use services such as BigQuery to prepare data; Vertex AI to train, deploy, monitor, and scale models in production; as well as MLOps to automate the entire process. By the end of this book, you will be able to unlock the full potential of Google Cloud's AI/ML offerings. What You Will Learn: - Build solutions with open-source offerings on Google Cloud, such as TensorFlow, PyTorch, and Spark - Source, understand, and prepare data for ML workloads - Build, train, and deploy ML models on Google Cloud - Create an effective MLOps strategy and implement MLOps workloads on Google Cloud - Discover common challenges in typical AI/ML projects and get solutions from experts - Explore vector databases and their importance in Generative AI applications - Uncover new Gen AI patterns such as Retrieval Augmented Generation (RAG), agents, and agentic workflows Who this book is for: This book is for aspiring solutions architects looking to design and implement AI/ML solutions on Google Cloud. Although this book is suitable for both beginners and experienced practitioners, basic knowledge of Python and ML concepts is required. The book focuses on how AI/ML is used in the real world on Google Cloud. It briefly covers the basics at the beginning to establish a baseline for you, but it does not go into depth on the underlying mathematical concepts that are readily available in academic material. Table of Contents - AI/ML Concepts, Real-World Applications, and Challenges - Understanding the ML Model Development Lifecycle - AI/ML Tooling and the Google Cloud AI/ML Landscape - Utilizing Google Cloud's High-Level AI Services - Building Custom ML Models on Google Cloud - Diving Deeper-Preparing and Processing Data for AI/ML Workloads on Google Cloud - Feature Engineering and Dimensionality Reduction - Hyperparameters and Optimization - Neural Networks and Deep Learning - (N.B. Please use the Read Sample option to see further chapters)
Autorenporträt
Kieran Kavanagh is a Principal Architect at Google. He works with large enterprises to guide them on architecting solutions to meet their business needs on Google Cloud. Having spent over a decade and a half working as a Solutions Architect at some of the world's largest technology companies, such as Amazon, AT&T, Ericsson, and Google, he has amassed a wealth of knowledge in architecting extremely large-scale and highly complex technology solutions. He has presented on these topics at more than 100 technology conferences all over the world. Prior to joining Google, he was a Principal AI/ML Solutions Architect in Strategic Accounts at AWS, working with AWS' largest customers to design and build cutting-edge and global-scale AI/ML solutions. He has a passion for AI/ML, and for teaching and helping others to grow their careers in this industry. ¿Originally from Cork, Ireland, Kieran has lived and worked in many countries around the world, and he now resides in Atlanta, GA.