Machine Learning with BigQuery ML: Create, execute, and improve machine learning models in BigQuery using standard SQL queries

BIRMINGHAM—MUMBAI

Machine Learning with BigQuery ML

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Contributors

About the author

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!

About the reviewers

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.

Table of Contents

Preface

Section 1: Introduction and Environment Setup

Chapter 1: Introduction to Google Cloud and BigQuery

Introducing Google Cloud Platform

Interacting with GCP

Discovering GCP's key differentiators

Exploring AI and ML services on GCP

Core platform services

Building blocks

Solutions

Introducing BigQuery

BigQuery architecture

BigQuery's advantages over traditional data warehouses

Interacting with BigQuery

BigQuery data structures

Discovering BigQuery ML

BigQuery ML benefits

BigQuery ML algorithms

Understanding BigQuery pricing

BigQuery pricing

BigQuery ML pricing

Free operations and free tiers

Pricing calculator

Summary

Further resources

Chapter 2: Setting Up Your GCP and BigQuery Environment

Technical requirements

Creating your GCP account and project

Registering a GCP account

Exploring Google Cloud Console

Creating a GCP project

Activating BigQuery

Discovering the BigQuery web UI

Exploring the BigQuery public datasets

Searching for a public dataset

Analyzing a table

Summary

Further reading

Chapter 3: Introducing BigQuery Syntax

Technical requirements

Creating a BigQuery dataset

Discovering BigQuery SQL

CRUD operations

Diving into BigQuery ML

Summary

Further resources

Section 2: Deep Learning Networks

Chapter 4: Predicting Numerical Values with Linear Regression

Technical requirements

Introducing the business scenario

Discovering linear regression

Exploring and understanding the dataset

Understanding the data

Checking the data's quality

Segmenting the dataset

Training the linear regression model

Evaluating the linear regression model

Utilizing the linear regression model

Drawing business conclusions

Summary

Further reading

Chapter 5: Predicting Boolean Values Using Binary Logistic Regression

Technical requirements

Introducing the business scenario

Discovering binary logistic regression

Exploring and understanding the dataset

Understanding the data

Segmenting the dataset

Training the binary logistic regression model

Evaluating the binary logistic regression model

Using the binary logistic regression model

Drawing business conclusions

Summary

Further resources

Chapter 6: Classifying Trees with Multiclass Logistic Regression

Technical requirements

Introducing the business scenario

Discovering multiclass logistic regression

Exploring and understanding the dataset

Understanding the data

Checking the data quality

Segmenting the dataset

Training the multiclass logistic regression model

Evaluating the multiclass logistic regression model

Using the multiclass logistic regression model

Drawing business conclusions

Summary

Further resources

Section 3: Advanced Models with BigQuery ML

Chapter 7: Clustering Using the K-Means Algorithm

Technical requirements

Introducing the business scenario

Discovering K-Means clustering

Exploring and understanding the dataset

Understanding the data

Checking the data quality

Creating the training datasets

Training the K-Means clustering model

Evaluating the K-Means clustering model

Using the K-Means clustering model

Drawing business conclusions

Summary

Further resources

Chapter 8: Forecasting Using Time Series

Technical requirements

Introducing the business scenario

Discovering time series forecasting

Exploring and understanding the dataset

Understanding the data

Checking the data quality

Creating the training dataset

Training the time series forecasting model

Evaluating the time series forecasting model

Using the time series forecasting model

Presenting the forecast

Summary

Further resources

Chapter 9: Suggesting the Right Product by Using Matrix Factorization

Technical requirements

Introducing the business scenario

Discovering matrix factorization

Configuring BigQuery Flex Slots

Exploring and preparing the dataset

Understanding the data

Creating the training dataset

Training the matrix factorization model

Evaluating the matrix factorization model

Using the matrix factorization model

Drawing business conclusions

Summary

Further resources

Chapter 10: Predicting Boolean Values Using XGBoost

Technical requirements

Introducing the business scenario

Discovering the XGBoost Boosted Tree classification model

Exploring and understanding the dataset

Checking the data quality

Segmenting the dataset

Training the XGBoost classification model

Evaluating the XGBoost classification model

Using the XGBoost classification model

Drawing business conclusions

Summary

Further resources

Chapter 11: Implementing Deep Neural Networks

Technical requirements

Introducing the business scenario

Discovering DNNs

DNNs in BigQuery ML

Preparing the dataset

Training the DNN models

Evaluating the DNN models

Using the DNN models

Drawing business conclusions

Deep neural networks versus linear models

Summary

Further resources

Section 4: Further Extending Your ML Capabilities with GCP

Chapter 12: Using BigQuery ML with AI Notebooks

Technical requirements

Discovering AI Platform Notebooks

AI Platform Notebooks pricing

Configuring the first notebook

Implementing BigQuery ML models within notebooks

Compiling the AI notebook

Running the code in the AI notebook

Summary

Further resources

Chapter 13: Running TensorFlow Models with BigQuery ML

Technical requirements

Introducing TensorFlow

Discovering the relationship between BigQuery ML and TensorFlow

Understanding commonalities and differences

Collaborating with BigQuery ML and TensorFlow

Converting BigQuery ML models into TensorFlow

Training the BigQuery ML to export it

Exporting the BigQuery ML model

Running TensorFlow models with BigQuery ML

Summary

Further resources

Chapter 14: BigQuery ML Tips and Best Practices

Choosing the right BigQuery ML algorithm

Preparing the datasets

Working with high-quality data

Segmenting the datasets

Understanding feature engineering

Tuning hyperparameters

Using BigQuery ML for online predictions

Summary

Further resources

Other Books You May Enjoy

Preface

Machine Learning (ML) democratization is one of the fastest growing trends in the AI industry. In this field, BigQuery ML represents a fundamental tool for bridging the gap between data analysis and the implementation of innovative ML models. Through this book, you will have the opportunity to learn how to use BigQuery and BigQuery ML with an incremental approach that combines technical explanations with hands-on exercises. Following a brief introduction, you will immediately be able to build ML models on concrete use cases using BigQuery ML. By the end of this book, you will be able to choose the right ML algorithm to train, evaluate, and use advanced ML models.

Who this book is for

This book is for data scientists, data analysts, data engineers, and anyone looking to get started with Google's BigQuery ML. You'll 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. A basic knowledge of BigQuery and SQL is required.

What this book covers

Chapter 1, Introduction to Google Cloud and BigQuery, provides an overview of the Google Cloud Platform and of the BigQuery analytics database.

Chapter 2, Setting Up Your GCP and BigQuery Environment, explains the configuration of your first Google Cloud account, project, and BigQuery environment.

Chapter 3, Introducing BigQuery Syntax, covers the main SQL operations for working on BigQuery.

Chapter 4, Predicting Numerical Values with Linear Regression, explains the development of a linear regression ML model to predict the trip durations of a bike rental service.

Chapter 5, Predicting Boolean Values Using Binary Logistic, explains the implementation of a binary logistic regression ML model to predict the behavior of a taxi company's customers.

Chapter 6, Classifying Trees with Multiclass Logistic Regression, explains the development of a multiclass logistic ML algorithm to automatically classify species of trees according to their natural characteristics.

Chapter 7, Clustering Using the K-Means Algorithm, covers the implementation of a clustering system to identify the best-performing drivers in a taxi company.

Chapter 8, Forecasting Using Time Series, outlines the design and implementation of a forecasting tool to predict and present the sales of specific products.

Chapter 9, Suggesting the Right Product by Using Matrix Factorization, explains how to build a recommendation engine, using the matrix factorization algorithm, that suggests the best product to each customer.

Chapter 10, Predicting Boolean Values Using XGBoost, covers the implementation of a boosted tree ML model to predict the behavior of a taxi company's customers.

Chapter 11, Implementing Deep Neural Networks, covers the design and implementation of a Deep Neural Network (DNN) to predict the trip durations of a bike rental service.

Chapter 12, Using BigQuery ML with AI Notebooks, explains how AI Platform Notebooks can be integrated with BigQuery ML to develop and share ML models.

Chapter 13, Running TensorFlow Models with BigQuery ML, explains how BigQuery ML and TensorFlow can work together.

Chapter 14, BigQuery ML Tips and Best Practices, covers ML best practices and tips that can be applied during the development of a BigQuery ML model.

To get the most out of this book

You will need to have a basic knowledge of SQL syntax and some experience of using databases.

A knowledge of the fundamentals of ML is not mandatory but is advised.

If you are using the digital version of this book, we advise you to type the code yourself or access the code via the GitHub repository (link available in the next section). Doing so will help you to avoid any potential errors related to the copying and pasting of code.

Download the example code files

You can download the example code files for this book from GitHub at https://github.com/PacktPublishing/Machine-Learning-with-BigQuery-ML. In case there's an update to the code, it will be updated on the existing GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Code in Action

Code in Action videos for this book can be viewed at https://bit.ly/3f11XbU.

Download the color images

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://static.packt-cdn.com/downloads/9781800560307_ColorImages.pdf.

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: Sort the results of a query according to a specific list of fields with the ORDER BY clause.

A block of code is set as follows:

UPDATE

    `bigqueryml-packt.03_bigquery_syntax.first_table`

SET

    description= 'This is my updated description'

WHERE

    id_key=1;

Bold: Indicates a new term, an important word, or words that you see on screen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: BigQuery supports two different SQL dialects: standard SQL and legacy SQL.

Tips or important notes

Appear like this.

Get in touch

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Section 1: Introduction and Environment Setup

This section provides an introduction to machine learning and an overview of the technical tools that will be used in the next sections of the book: Google Cloud Platform, BigQuery, and BigQuery ML, as well as the SQL syntax related to it.

This section comprises the following chapters:

Chapter 1, Introduction to Google Cloud and BigQuery

Chapter 2, Setting Up Your GCP and BigQuery Environment

Chapter 3, Introducing BigQuery Syntax

Chapter 1: Introduction to Google Cloud and BigQuery

The adoption of the public cloud enables companies and users to access innovative and cost-effective technologies. This is particularly valuable in the big data and Artificial Intelligence (AI) areas, where new solutions are providing possibilities that seemed impossible to achieve with on-premises systems only a few years ago. In order to be effective in the day-to-day business of a company, the new AI capabilities need to be shared between different roles and not concentrated only with technicians. Most cloud providers are currently addressing the challenge of democratizing AI across different departments and employees with different skills.

In this context, Google Cloud provides several services to accelerate the processing of large amounts of data and build Machine Learning (ML) applications that can make better decisions.

In this chapter, we'll gradually introduce the main concepts that will be useful in the upcoming hands-on activities. Using an incremental approach, we'll go through the following topics:

Introducing Google Cloud Platform

Exploring AI and ML services on GCP

Introducing BigQuery

Discovering BigQuery ML

Understanding BigQuery pricing

Introducing Google Cloud Platform

Starting from 1998 with the launch of Google Search, Google has developed one of the largest and most powerful IT infrastructures in the world. Today, this infrastructure is used by billions of users to use services such as Gmail, YouTube, Google Photo, and Maps. After 10 years, in 2008, Google decided to open its network and IT infrastructure to business customers, taking an infrastructure that was initially developed for consumer applications to public service and launching Google Cloud Platform (GCP).

The 90+ services that Google currently provides to large enterprises and small- and medium-sized businesses cover the following categories:

Compute: Used to support workloads or applications with virtual machines such as Google Compute Engine, containers with Google Kubernetes Engine, or platforms such as AppEngine.

Storage and databases: Used to store datasets and objects in an easy and convenient way. Some examples are Google Cloud Storage, Cloud SQL, and Spanner.

Networking: Used to easily connect different locations and data centers across the globe with Virtual Private Clouds (VPCs), firewalls, and fully managed global routers.

Big data: Used to store and process large amounts of information in a structured, semi-structured, or unstructured format. Among these services are Google DataProc, the Hadoop services offered by GCP, and BigQuery, which is the main focus of this book.

AI and machine learning: This product area provides various tools for different kinds of users, enabling them to leverage AI and ML in their everyday business. Some examples are TensorFlow, AutoML, Vision APIs, and BigQuery ML, the main focus of this book.

Identity, security, and management tools: This area includes all the services that are necessary to prevent unauthorized access, ensure security, and monitor all other cloud infrastructure. Identity Access Management, Key Management Service, Cloud Logging, and Cloud Audit Logs are just some of these tools.

Internet of Things (IoT): Used to connect plants, vehicles, or any other objects to the GCP infrastructure, enabling the development of modern IoT use cases. The core component of this area is Google IoT Core.

API management: Tools to expose services to customers and partners through REST APIs, providing the ability to fully leverage the benefits of interconnectivity. In this pillar, Google Apigee is one of the most famous products and is recognized as the leader of this market segment.

Productivity: Used to improve productivity and collaboration for all companies that want to start working with Google and embracing its way of doing business through the powerful tools of Google Workplace (previously GSuite).

Interacting with GCP

All the services just mentioned can be accessed through four different interfaces:

Google Cloud Console: The web-based user interface of GCP, easily accessible from compatible web browsers such as Google Chrome, Edge, or Firefox. For the hands-on exercises in this book, we'll mainly use Google Cloud Console:

Figure 1.1 – Screenshot of Google