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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

Generating predictions

Once the model deployment is complete, you will see the endpoint listed in the DEPLOY & TEST tab. Underneath that, there will be a Test your model table, listing all feature values required to generate predictions. Fields will already have starting values based on the data used for AutoML training, but you can type in different values and click Predict to generate predictions.

Figure 5.21 – Testing a deployed model

Figure 5.21 – Testing a deployed model

Once the model churns through the provided feature values, it will return the confidence score associated with each label. The one with the highest confidence score is the predicted label.

Figure 5.22 – The prediction result

Figure 5.22 – The prediction result

Now let’s look at the options available to developers to use the Vertex AI models programmatically.

Generating predictions programmatically

To access the Vertex AI prediction service, you can work with the Vertex AI SDK for Python, or client...

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