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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

Google Machine Learning and Generative AI for Solutions Architects: ​Build efficient and scalable AI/ML solutions on Google Cloud Paperback / softback - 2024

by Kieran Kavanagh, O.C.D

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Paperback / softback. New. ?Learn how to architect and run real-world AI/ML solutions at scale on Google Cloud, as well as best practices, common industry challenges, and how to address those challenges effectively. Key Features Understand the AI/ML landscape on Google Cloud Learn Data preparation and model development Implement MLOps and scaling production workloads with Google Cloud Book Description​Almost every company nowadays is either using or trying to use AI/ML in some way. While AI/ML research is undoubtedly complex, what is often more complex is actually building and running applications that use AI/ML effectively. This book teaches you how to successfully design and run AI/ML workloads, based on years of experience implementing large-scale and highly complex AI/ML projects at some of the world’s leading technology companies. ​Understand the common challenges that companies run into when implementing AI/ML workloads, and industry-proven best practices to overcome those challenges. Learn about the vast AI/ML landscape on Google Cloud and how to implement all of the required steps in a typical AI/ML project. Use services such as BigQuery to prepare data, and Vertex AI to train, deploy, monitor, and scale models in production, as well as MLOps to automate the entire process. ​Suitable both for beginners and experienced practitioners, it begins by covering important fundamental AI/ML concepts, and then builds in complexity through examples and hands-on activities to eventually dive deep into advanced, cutting-edge AI/ML applications that address real-world use-cases in today’s market.What you will learn ?Learn about the various AI/ML offerings on Google Cloud, and how they can be used to address specific business problems ?Learn how to source, understand, and prepare data for ML workloads ?Build, train, and deploy ML models on Google Cloud ?Learn how to build an effective MLOps strategy and implement MLOps workloads on Google Cloud Who this book is forPeople aspiring to become Solution Architects, who want to know how to design and implement AI/ML solutions on Google Cloud. Basic knowledge of Python and ML concept required. This book will briefly cover the basics at the beginning in order to establish a baseline for the readers, but it will not go into depth on the underlying mathematical concepts that the readers could learn from academic materials. It will focus on how to use AI/ML in the real world on Google Cloud
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Details

  • Title Google Machine Learning and Generative AI for Solutions Architects: ​Build efficient and scalable AI/ML solutions on Google Cloud
  • Author Kieran Kavanagh, O.C.D
  • Binding Paperback
  • Condition New
  • Pages 552
  • Volumes 1
  • Language ENG
  • Publisher Packt Publishing
  • Publication date 2024-06-28
  • Bookseller's Inventory # B9781803245270
  • ISBN 9781803245270 / 1803245271
  • Weight 2.07 lbs (0.94 kg)
  • Dimensions 9.25 x 7.5 x 1.12 in (23.50 x 19.05 x 2.84 cm)
  • Category Computers - General Information
  • Quantity available 10

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From the publisher

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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