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

BERT-based fake news classification

In our first experiment, we trained a classical random forest classifier on TF-IDF features to detect fake versus real news articles and got an accuracy score of about 93%. In this section, we will train a deep learning model for the same task and see if we get any accuracy gains over the classical tree-based approach. Deep learning has changed the way we used to solve NLP problems. Classical approaches required hand-crafted features, most of which were related to the frequency of words appearing in a document. Looking at the complexity of languages, just knowing the count of words in a paragraph is not enough. The order in which words occur also has a significant impact on the overall meaning of the paragraph or sentence. Deep learning approaches such as Long-Short-Term-Memory (LSTM) also consider the sequential dependency of words in sentences or paragraphs to get a more meaningful feature representation. LSTM has achieved great success in many...