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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

Building a basic deep learning model with TensorFlow

TensorFlow, or TF for short, is an end-to-end platform for building ML models. The main focus of the TensorFlow framework is to simplify the development, training, evaluation, and deployment of deep neural networks. When it comes to working with unstructured data (such as images, videos, audio, etc.), neural network-based solutions have achieved significantly better results than traditional ML approaches that mostly rely on handcrafted features. Deep neural networks are good at understanding complex patterns from high-dimensional data points (for example, an image with millions of pixels). In this section, we will develop a basic neural network-based model using TensorFlow. In the next few sections, we will see how Vertex AI can help with setting up scalable and systemic training/tuning of such custom models.

Important Note

It is important to note that TensorFlow is not the only ML framework that Vertex AI supports. Vertex...

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