Google Machine Learning and Generative AI for Solutions Architects
¿Build efficient and scalable AI/ML solutions on Google Cloud
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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: Most companies today are incorporating AI...
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: Most companies today are incorporating AI/ML into their businesses. Building and running apps utilizing AI/ML effectively is tough. This book, authored by a principal architect with about two decades of industry experience, who has led cross-functional teams to design, plan, implement, and govern enterprise cloud strategies, shows you exactly how to design and run AI/ML workloads successfully using years of experience from some of the world's leading tech companies. You'll get a clear understanding of essential fundamental AI/ML concepts, before moving on to complex topics with the help of examples and hands-on activities. This will help you explore advanced, cutting-edge AI/ML applications that address real-world use cases in today's market. You'll recognize the common challenges that companies face when implementing AI/ML workloads, and discover industry-proven best practices to overcome these. 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 - Deploying, Monitoring, and Scaling in Production - Machine Learning Engineering and MLOps with GCP (N.B. Please use the Read Sample option to see further chapters)
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