Google Machine Learning and Generative AI for Solutions Architects

<p><strong>Architect and run real-world AI/ML solutions at scale on Google Cloud and discover best practices to address common industry challenges effectively</strong></p><p><strong>Key Features:</strong></p><p>- Understand key concepts from fundamentals through to complex topics via a methodical approach</p><p>- Build real-world end-to-end MLOps solutions and generative AI applications on Google Cloud</p><p>- Get your hands on a code repository with over 20 hands-on projects for all stages of the ML model development lifecycle</p><p>- Purchase of the print or Kindle book includes a free PDF eBook</p><p><strong>Book Description:</strong></p><p>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.</p><p>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.</p><p>By the end of this book you will be able to unlock the full potential of Google Cloud's AI/ML offerings.</p><p><strong>What You Will Learn:</strong></p><p>- Build solutions with open-source offerings on Google Cloud such as TensorFlow PyTorch and Spark</p><p>- Source understand and prepare data for ML workloads</p><p>- Build train and deploy ML models on Google Cloud</p><p>- Create an effective MLOps strategy and implement MLOps workloads on Google Cloud</p><p>- Discover common challenges in typical AI/ML projects and get solutions from experts</p><p>- Explore vector databases and their importance in Generative AI applications</p><p>- Uncover new Gen AI patterns such as Retrieval Augmented Generation (RAG) agents and agentic workflows</p><p><strong>Who this book is for:</strong></p><p>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.</p><p><strong>Table of Contents</strong></p><p>- AI/ML Concepts Real-World Applications and Challenges</p><p>- Understanding the ML Model Development Lifecycle</p><p>- AI/ML Tooling and the Google Cloud AI/ML Landscape</p><p>- Utilizing Google Cloud's High-Level AI Services</p><p>- Building Custom ML Models on Google Cloud</p><p>- Diving Deeper-Preparing and Processing Data for AI/ML Workloads on Google Cloud</p><p>- Feature Engineering and Dimensionality Reduction</p><p>- Hyperparameters and Optimization</p><p>- Neural Networks and Deep Learning</p><p>- Deploying Monitoring and Scaling in Production</p><p>- Machine Learning Engineering and MLOps with GCP</p><p><strong>(N.B. Please use the Read Sample option to see further chapters)</strong></p>