The Definitive Guide to Google Vertex AI Accelerate your machine learning journey ...

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Free Download The Definitive Guide to Google Vertex AI
by Jasmeet Bhatia
| Kartik Chaudhary

English | 2024 | ISBN: 1801815267 | 422 pages | True/Retail PDF EPUB | 30.55 MB
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.


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