Alessandro Marrandino - Machine Learning with BigQuery ML: Create, execute, and improve machine learning models in BigQuery using standard SQL queries
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- Book:Machine Learning with BigQuery ML: Create, execute, and improve machine learning models in BigQuery using standard SQL queries
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Manage different business scenarios with the right machine learning technique using Googles highly scalable BigQuery ML
Key Features- Gain a clear understanding of AI and machine learning services on GCP, learn when to use these, and find out how to integrate them with BigQuery ML
- Leverage SQL syntax to train, evaluate, test, and use ML models
- Discover how BigQuery works and understand the capabilities of BigQuery ML using examples
BigQuery ML enables you to easily build machine learning (ML) models with SQL without much coding. This book will help you to accelerate the development and deployment of ML models with BigQuery ML.
The book starts with a quick overview of Google Cloud and BigQuery architecture. Youll then learn how to configure a Google Cloud project, understand the architectural components and capabilities of BigQuery, and find out how to build ML models with BigQuery ML. The book teaches you how to use ML using SQL on BigQuery. Youll analyze the key phases of a ML models lifecycle and get to grips with the SQL statements used to train, evaluate, test, and use a model. As you advance, youll build a series of use cases by applying different ML techniques such as linear regression, binary and multiclass logistic regression, k-means, ARIMA time series, deep neural networks, and XGBoost using practical use cases. Moving on, youll cover matrix factorization and deep neural networks using BigQuery MLs capabilities. Finally, youll explore the integration of BigQuery ML with other Google Cloud Platform components such as AI Platform Notebooks and TensorFlow along with discovering best practices and tips and tricks for hyperparameter tuning and performance enhancement.
By the end of this BigQuery book, youll be able to build and evaluate your own ML models with BigQuery ML.
What you will learn- Discover how to prepare datasets to build an effective ML model
- Forecast business KPIs by leveraging various ML models and BigQuery ML
- Build and train a recommendation engine to suggest the best products for your customers using BigQuery ML
- Develop, train, and share a BigQuery ML model from previous parts with AI Platform Notebooks
- Find out how to invoke a trained TensorFlow model directly from BigQuery
- Get to grips with BigQuery ML best practices to maximize your ML performance
This book is for data scientists, data analysts, data engineers, and anyone looking to get started with Googles BigQuery ML. Youll also find this book useful if you want to accelerate the development of ML models or if you are a business user who wants to apply ML in an easy way using SQL. Basic knowledge of BigQuery and SQL is required.
Table of Contents- Introduction to Google Cloud and BigQuery
- Setting Up Your GCP and BigQuery Environment
- Introducing BigQuery Syntax
- Predicting Numerical Values with Linear Regression
- Predicting Boolean Values Using Binary Logistic Regression
- Classifying Trees with Multiclass Logistic Regression
- Clustering Using the K-Means Algorithm
- Forecasting Using Time Series
- Suggesting the Right Product by Using Matrix Factorization
- Predicting Boolean Values Using XGBoost
- Implementing Deep Neural Networks
- Using BigQuery ML with AI Notebooks
- Running TensorFlow Models with BigQuery ML
- BigQuery ML Tips and Best Practices
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Create, execute, and improve machine learning models in BigQuery using standard SQL queries
Alessandro Marrandino
BIRMINGHAMMUMBAI
Copyright 2021 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
Group Product Manager: Kunal Parikh
Publishing Product Manager: Sunith Shetty
Senior Editor: David Sugarman
Content Development Editor: Nathanya Dias
Technical Editor: Manikandan Kurup
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Production Designer: Prashant Ghare
First published: June 2021
Production reference: 1120521
Published by Packt Publishing Ltd.
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ISBN 978-1-80056-030-7
www.packt.com
Alessandro Marrandino is a Google Cloud customer engineer. He helps various enterprises on the digital transformation to adopt cloud technologies. He is actively focused on and experienced in data management and smart analytics solutions. He has spent his entire career on data and artificial intelligence projects for global companies in different industries.
I want to thank the people who have been close to me and supported me, especially my wife, Federica. Thanks to her love and availability, I was able to dedicate most of my free time to writing this book, while we were waiting for the most important person in our life: Eva. Special thanks go to all my family. They have always believed in me and in my passion for technology and data. Just a final remark for my mom: The internet has had some success and there are people working on it!
Marijan Milovec currently works as a software developer. He is highly ambitious and interested in software development, DevOps, and software architecture. He is also the lead organizer of the Google Developer Group Zagreb, which focuses on software development, software architecture, artificial intelligence, machine learning, deep learning, data science, DevOps, Docker, Kubernetes, Google Cloud, and more.
Sathish VJ is a software architect, technology trainer, and angel investor. He has all the open certifications on Google Cloud, including Google Cloud Machine Learning Engineer, and is also a Google Cloud Authorized Trainer. He runs a YouTube channel, called AwesomeGCP, where he teaches people how to apply Google Cloud to their projects and prepare for certifications.
Sharmistha Chatterjee is a data science evangelist with 15+ years of professional experience in the field of machine learning (AI research and productionizing scalable solutions) and cloud applications. She has worked in both Fortune 500 companies, as well as in very early-stage startups. She is currently working as a Senior Manager of Data Sciences at Publicis Sapient where she leads the digital transformation of clients across industry verticals. She is an active blogger, an international speaker at various tech conferences, and 2X Google Developer Expert in Machine Learning and Google Cloud. She is also the Hackernoon Tech award winner for 2020, been listed as 40 under 40. Data Scientist by AIM and '21 tech trailblazers 2021 by Google.
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