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Key benefits

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

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.

Who is this book 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.

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

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Colour book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

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Frequently bought together

Data Engineering with Google Cloud Platform

$41.99

Google Machine Learning and Generative AI for Solutions Architects

$49.99

Database Design and Modeling with Google Cloud

$34.99

Total $ 126.97

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covey Jul 02, 2024

This book is one of the most comprehensive courses on AI in the cloud that I've encountered so far! I love how it starts with the basics and then builds incrementally to cover advanced topics. This makes it accessible for almost anybody to start learning the concepts. I'm a solutions architect with a lot of experience in the tech industry, and I really enjoyed how the book covers the theoretical concepts in each chapter, and then provides guidance on how to actually build these systems in the cloud, along with best practices to optimize the implementation in terms of efficiency, reliability, cost, and other important architectural factors.My 13-year-old is also learning from this book, so it really does appeal to a broad audience!

Amazon Verified review

S.Kundu Aug 29, 2024

Google Machine Learning and Generative AI for Solutions Architects" provides an introduction to foundational AI/ML concepts and Google Cloud's tools, guiding readers through practical applications, custom model building and data preparation techniques. It covers model deployment, MLOps practices and addresses fairness, bias and explainability in AI models. The book concludes with a comprehensive overview of generative AI, including its evolution, applications and advanced techniques.A few important topics of the book that I want to highlight are as below:The book begins with introduction to foundational AI/ML concepts and explores various real-world applications and challenges, laying the groundwork for understanding more advanced topics in the book along with explaining ML Model Development Life CycleNext, it provides provides an overview of setting up and utilizing Google Cloud AI/ML services, including an introduction to the platform's tools and capabilities.It then focuses on practical applications of high-level AI services for common tasks such as image recognition and sentiment analysis.The book guides readers through building custom machine learning models on Google Cloud, using popular libraries like scikit-learn along with Vertex AI.It further covers data preparation techniques for AI/ML, including building both batch and streaming data pipelines on Google Cloud and discusses techniques for feature engineering and dimensionality reduction, highlighting tools such as PCA, LDA and the Vertex AI Feature StoreThe book then explores the concept of hyperparameters and strategies for hyperparameter optimization, providing hands-on examples with Vertex AI and also introduces neural networks and deep learning concepts, including model implementation in TensorFlow and challenges in optimizing neural networks.The book covers deployment strategies, monitoring and scaling models in production environments, including A/B testing and edge optimization and discusses the principles of MLOps (Machine Learning Operations) and how to implement them using tools like Vertex AI Pipelines for efficient model management.It then examines critical issues around bias, fairness and explainability in AI models, as well as the importance of lineage in tracking model development and focuses on governance practices and the architecture framework necessary for managing AI/ML workloads on Google Cloud.Finally, the book covers the concepts and techniques of generative AI, discussing its evolution and applications along with more advanced generative AI techniques, providing insights into state-of-the-art models and their practical uses.

Vipin Jul 07, 2024

This book is a must-read for anyone working with AI/ML on Google Cloud. As a practitioner in the field, I found this book to be both comprehensive and incredibly practical.The chapters skillfully guide you through essential AI/ML concepts, which is extremely helpful if you're new to the field. The book also focuses on real-world challenges in building AI applications and their solutions. The insights shared are invaluable, and access to a repo of hands-on projects provide a fantastic way to apply the knowledge gained.I was particularly impressed with the detailed exploration of Google Cloud's AI/ML tools and services. The step-by-step instructions on everything from data preparation to model deployment are clear and easy to follow.Whether you're a beginner or an experienced practitioner, this book has something for everyone. Highly recommended!

Damien Jul 05, 2024

An exceptional textbook! It breaks down complex concepts into easily digestible sections. It starts with the most basic principles for beginners and the chapters increase in complexity as you develop your understanding. A must for any capability level and an ideal intro for the next generation to gain knowledge in what will be an integrall part of their lives. Highly recommend!

Steven Fernandes Aug 06, 2024

An essential guide for leveraging Google Cloud's open-source offerings like TensorFlow, PyTorch, and Spark to build machine learning solutions. This book covers sourcing and preparing data, constructing and deploying ML models, and crafting an effective MLOps strategy on Google Cloud. It provides insights into overcoming common challenges in AI/ML projects with expert solutions. Additionally, the book explores advanced topics such as the use of vector databases in generative AI applications and introduces new generative AI patterns like Retrieval Augmented Generation, agents, and agentic workflows, making it a comprehensive resource for professionals in the field.

Google Machine Learning and Generative AI for Solutions Architects: Build efficient and scalable AI/ML solutions on Google Cloud

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