Google Machine Learning and Generative AI for Solutions Architects
English | 2024 | ISBN: 1803245271 | 552 pages | EPUB (True) | 21.85 MB
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
(N.B. Please use the Read Sample option to see further chapters)
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