The Definitive Guide to google Vertex AI: Accelerate your machine learning journey with google Cloud Vertex AI and MLOps best practices


The Definitive Guide to Google Vertex AI: Accelerate your machine learning journey with Google Cloud Vertex AI and MLOps best practices
by 作者: Jasmeet Bhatia (Author), Kartik Chaudhary (Author)
Publisher Finelybook 出版社: Packt Publishing
Publication Date 出版日期: 2023-12-29
Language 语言: English
Pages 页数: 422 pages
ISBN-10 书号: 1801815267
ISBN-13 书号: 9781801815260


Book Description

Implement machine learning pipelines with Google Cloud Vertex AI


Key Features

  • Understand the role of an AI platform and MLOps practices in machine learning projects
  • Get acquainted with Google Vertex AI tools and offerings that help accelerate the creation of end-to-end ML solutions
  • Implement Vision, NLP, and recommendation-based real-world ML models on Google Cloud Platform
  • Purchase of the print or Kindle book includes a free PDF eBook


Book Description

While AI has become an integral part of every organization today, the development of large-scale ML solutions and management of complex ML workflows in production continue to pose challenges for many. Google’s unified data and AI platform, Vertex AI, directly addresses these challenges with its array of MLOPs tools designed for overall workflow management.

This book is a comprehensive guide that lets you explore Google Vertex AI’s easy-to-advanced level features for end-to-end ML solution development. Throughout this book, you’ll discover how Vertex AI empowers you by providing essential tools for critical tasks, including data management, model building, large-scale experimentations, metadata logging, model deployments, and monitoring. You’ll learn how to harness the full potential of Vertex AI for developing and deploying no-code, low-code, or fully customized ML solutions. This book takes a hands-on approach to developing u deploying some real-world ML solutions on Google Cloud, leveraging key technologies such as Vision, NLP, generative AI, and recommendation systems. Additionally, this book covers pre-built and turnkey solution offerings as well as guidance on seamlessly integrating them into your ML workflows.

By the end of this book, you’ll have the confidence to develop and deploy large-scale production-grade ML solutions using the MLOps tooling and best practices from Google.


What you will learn

  • Understand the ML lifecycle, challenges, and importance of MLOps
  • Get started with ML model development quickly using Google Vertex AI
  • Manage datasets, artifacts, and experiments
  • Develop no-code, low-code, and custom AI solution on Google Cloud
  • Implement advanced model optimization techniques and tooling
  • Understand pre-built and turnkey AI solution offerings from Google
  • Build and deploy custom ML models for real-world applications
  • Explore the latest generative AI tools within Vertex AI


Who this book is for

If you are a machine learning practitioner who wants to learn end-to-end ML solution development on Google Cloud Platform using MLOps best practices and tools offered by Google Vertex AI, this is the book for you.

Table of
Contents

  1. Machine Learning project lifecycle and challenges
  2. What is ML Ops and why is it important for every organization using ML?
  3. It's all about Data - Options to store and transform ML datasets
  4. Vertex AI Workbench - One stop tool for all your ML Development needs
  5. Training ML Models - No code option - AutoML
  6. Training ML Models -Low code option - BQML
  7. Training fully custom ML models with Vertex AI
  8. Model Explainability through Vertex AI
  9. Model optimizations – Hyper parameter tuning and NAS (TBD)
  10. Vertex AI Deployment and Automation tools – Orchestration through managed Kubeflow pipelines
  11. Vertex AI Governance Tools
  12. Vertex AI - Generative AI Tools
  13. Document AI – Customizable document processing solution
  14. ML APIs – Vision API, NLP API etc.
  15. Recommender Systems - Predict what movie would you like to watch?

(N.B. Please use the Look Inside option to see further chapters)

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