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  • Book Overview & Buying The Definitive Guide to Google Vertex AI
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The Definitive Guide to Google Vertex AI

The Definitive Guide to Google Vertex AI

By : Jasmeet Bhatia, Kartik Chaudhary
4.9 (8)
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The Definitive Guide to Google Vertex AI

The Definitive Guide to Google Vertex AI

4.9 (8)
By: Jasmeet Bhatia, Kartik Chaudhary

Overview of this book

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.
Table of Contents (24 chapters)
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1
Part 1:The Importance of MLOps in a Real-World ML Deployment
4
Part 2: Machine Learning Tools for Custom Models on Google Cloud
14
Part 3: Prebuilt/Turnkey ML Solutions Available in GCP
18
Part 4: Building Real-World ML Solutions with Google Cloud

Transforming data

Raw data present in real-world applications is often unstructured and noisy. Thus, it cannot be fed directly to machine learning algorithms. We often need to apply several transformations on raw data and convert it into a format that is well supported by machine learning algorithms. In this section, we will learn about multiple options for transforming data in a scalable and efficient way on Google Cloud.

Here are three common options for data transformation in the GCP environment:

  • Ad hoc transformation within Jupyter Notebooks
  • Cloud Data Fusion
  • Dataflow pipelines for scalable data transformations

Let’s learn about these three methods in more detail.

Ad hoc transformations within Jupyter Notebook

Machine learning algorithms are mathematical and can only understand numeric data. For example, in computer vision problems, images are converted into numerical pixel values before they’re fed into a model. Similarly, in the...

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