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

google Machine Learning and Generative AI for Solutions Architects: ​Build efficient and scalable AI/ML solutions on google Cloud
Author: Kieran Kavanagh (Author), Priyanka Vergadia (Foreword)
Publisher finelybook 出版社:‏ Packt Publishing
Publication Date 出版日期:‏ 2024-06-28
Language 语言: English
Print Length 页数: 552 pages
ISBN-10: 1803245271
ISBN-13: 9781803245270

Book Description

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)