Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying The Definitive Guide to Google Vertex AI
  • Table Of Contents Toc
  • Feedback & Rating feedback
The Definitive Guide to Google Vertex AI

The Definitive Guide to Google Vertex AI

By : Jasmeet Bhatia, Kartik Chaudhary
4.9 (8)
close
close
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)
close
close
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

Using BQML for feature transformations

Two types of feature preprocessing are supported by BQML:

  • Automatic preprocessing: During training, BQML carries out automatic preprocessing. For further details, please carries out automatic preprocessing like missing data imputation, one-hot encoding, and timestamp transformation and encoding.
  • Manual preprocessing: You can use the TRANSFORM clause provided by BQML to define customized preprocessing using manual preprocessing functions. These functions can also be utilized outside the TRANSFORM clause.

While BQML does support some feature engineering tasks, it has certain limitations compared to more flexible and feature-rich ML frameworks:

  • Limited preprocessing functions: BQML provides a basic set of SQL functions for data preprocessing, such as scaling and encoding. However, it may lack some advanced preprocessing techniques or specialized functions available in other ML libraries such as scikit-learn or TensorFlow...

Unlock full access

Continue reading for free

A Packt free trial gives you instant online access to our library of over 7000 practical eBooks and videos, constantly updated with the latest in tech
bookmark search playlist download font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete

Confirmation

Modal Close icon
claim successful

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY