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Google Machine Learning and Generative AI for Solutions Architects
Google Machine Learning and Generative AI for Solutions Architects

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

By Kieran Kavanagh
Can$50.99
Book Jun 2024 552 pages 1st Edition
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Google Machine Learning and Generative AI for Solutions Architects

Part 1:The Basics

This part establishes the baseline for the rest of the book. It covers all the basic topics that need to be understood to grasp the more complex concepts (and implement the workloads) that come later in the book. We begin by covering some of the fundamental concepts of AI/ML, and we discuss examples of how AI/ML is used in real-world use cases. Most importantly, we discuss common challenges that companies often run into when implementing AI/ML projects at scale and begin discussing how to address such challenges, forming the basis for deeper discussions throughout this book. Next, we outline the steps in a typical AI/ML project lifecycle, which will be used to form the overall structure of much of this book. We round out this part by introducing Google Cloud and common AI/ML tooling.

This part contains the following chapters:

  • Chapter 1, AI/ML Concepts, Real-World Applications, and Challenges
  • Chapter 2, Understanding the ML Model Development Life Cycle
  • Chapter 3, AI/ML Tooling and the Google Cloud AI/ML Landscape
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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

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

Product Details

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Publication date : Jun 28, 2024
Length 552 pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781803245270
Category :

What do you get with eBook?

Product feature icon Instant access to your Digital eBook purchase
Product feature icon Download this book in EPUB and PDF formats
Product feature icon Access this title in our online reader with advanced features
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Product Details


Publication date : Jun 28, 2024
Length 552 pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781803245270
Category :

Table of Contents

24 Chapters
Preface Chevron down icon Chevron up icon
1. Part 1:The Basics Chevron down icon Chevron up icon
2. Chapter 1: AI/ML Concepts, Real-World Applications, and Challenges Chevron down icon Chevron up icon
3. Chapter 2: Understanding the ML Model Development Life Cycle Chevron down icon Chevron up icon
4. Chapter 3: AI/ML Tooling and the Google Cloud AI/ML Landscape Chevron down icon Chevron up icon
5. Part 2:Diving in and building AI/ML solutions Chevron down icon Chevron up icon
6. Chapter 4: Utilizing Google Cloud’s High-Level AI Services Chevron down icon Chevron up icon
7. Chapter 5: Building Custom ML Models on Google Cloud Chevron down icon Chevron up icon
8. Chapter 6: Diving Deeper – Preparing and Processing Data for AI/ML Workloads on Google Cloud Chevron down icon Chevron up icon
9. Chapter 7: Feature Engineering and Dimensionality Reduction Chevron down icon Chevron up icon
10. Chapter 8: Hyperparameters and Optimization Chevron down icon Chevron up icon
11. Chapter 9: Neural Networks and Deep Learning Chevron down icon Chevron up icon
12. Chapter 10: Deploying, Monitoring, and Scaling in Production Chevron down icon Chevron up icon
13. Chapter 11: Machine Learning Engineering and MLOps with Google Cloud Chevron down icon Chevron up icon
14. Chapter 12: Bias, Explainability, Fairness, and Lineage Chevron down icon Chevron up icon
15. Chapter 13: ML Governance and the Google Cloud Architecture Framework Chevron down icon Chevron up icon
16. Chapter 14: Additional AI/ML Tools, Frameworks, and Considerations Chevron down icon Chevron up icon
17. Part 3:Generative AI Chevron down icon Chevron up icon
18. Chapter 15: Introduction to Generative AI Chevron down icon Chevron up icon
19. Chapter 16: Advanced Generative AI Concepts and Use Cases Chevron down icon Chevron up icon
20. Chapter 17: Generative AI on Google Cloud Chevron down icon Chevron up icon
21. Chapter 18: Bringing It All Together: Building ML Solutions with Google Cloud and Vertex AI Chevron down icon Chevron up icon
22. Index Chevron down icon Chevron up icon
23. Other Books You May Enjoy Chevron down icon Chevron up icon

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