Book Image

The Definitive Guide to Google Vertex AI

By : Jasmeet Bhatia, Kartik Chaudhary
4 (1)
Book Image

The Definitive Guide to Google Vertex AI

4 (1)
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)
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

What is NAS and how is it different from HPT?

Artificial Neural Networks or ANNs are widely used today for solving complex ML problems. Most of the time, these network architectures are hand-designed by ML experts, which may not be optimal every time. Neural Architecture Search or NAS is a technique that automates the process of designing neural network architectures that usually outperform hand-designed networks.

Although both HPT and NAS are used as model optimization techniques, there are certain differences in how they both work. HPT assumes a given architecture and focuses on optimizing the hyperparameters that lead to the best model. HPT optimizes hyperparameters such as learning rate, optimizer, batch size, activation function, and so on. NAS, on the other hand, focuses on optimizing architecture-specific parameters (in a way, it automates the process of designing a neural network architecture). NAS optimizes parameters such as the number of layers, number of units, types...