Machine Learning with BigQuery ML
eBook - ePub

Machine Learning with BigQuery ML

Alessandro Marrandino

  1. 344 páginas
  2. English
  3. ePUB (apto para móviles)
  4. Disponible en iOS y Android
eBook - ePub

Machine Learning with BigQuery ML

Alessandro Marrandino

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Manage different business scenarios with the right machine learning technique using Google's highly scalable BigQuery MLKey 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 examplesBook DescriptionBigQuery 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. You'll 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. You'll analyze the key phases of a ML model's lifecycle and get to grips with the SQL statements used to train, evaluate, test, and use a model. As you advance, you'll 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, you'll cover matrix factorization and deep neural networks using BigQuery ML's capabilities. Finally, you'll 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, you'll 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 performanceWho this book is forThis 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. Basic knowledge of BigQuery and SQL is required.

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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 Cloud Console
Figure 1.1 – Screenshot of Google Cloud Console
  • Google Cloud SDK: The client SDK can be installed in order to interact with GCP services through the command line. It can be very useful to automate tasks and operations by scheduling them into scripts.
  • Client libraries: The SDK also includes some client libraries to interact with GCP using the most common programming languages, such as Python, Java, and Node.js.
  • REST APIs: Any task or operation performed on GCP can be executed by invoking a specific REST API from any compatible software.
Now that we've learned how to interact with GCP, let's discover how GCP is different from other cloud providers.

Discovering GCP's key differentiators

GCP is not the only public cloud provider on the market. Oth...


  1. Machine Learning with BigQuery ML
  2. Contributors
  3. Preface
  4. Section 1: Introduction and Environment Setup
  5. Chapter 1: Introduction to Google Cloud and BigQuery
  6. Chapter 2: Setting Up Your GCP and BigQuery Environment
  7. Chapter 3: Introducing BigQuery Syntax
  8. Section 2: Deep Learning Networks
  9. Chapter 4: Predicting Numerical Values with Linear Regression
  10. Chapter 5: Predicting Boolean Values Using Binary Logistic Regression
  11. Chapter 6: Classifying Trees with Multiclass Logistic Regression
  12. Section 3: Advanced Models with BigQuery ML
  13. Chapter 7: Clustering Using the K-Means Algorithm
  14. Chapter 8: Forecasting Using Time Series
  15. Chapter 9: Suggesting the Right Product by Using Matrix Factorization
  16. Chapter 10: Predicting Boolean Values Using XGBoost
  17. Chapter 11: Implementing Deep Neural Networks
  18. Section 4: Further Extending Your ML Capabilities with GCP
  19. Chapter 12: Using BigQuery ML with AI Notebooks
  20. Chapter 13: Running TensorFlow Models with BigQuery ML
  21. Chapter 14: BigQuery ML Tips and Best Practices
  22. Other Books You May Enjoy
Estilos de citas para Machine Learning with BigQuery ML

APA 6 Citation

Marrandino, A. (2021). Machine Learning with BigQuery ML (1st ed.). Packt Publishing. Retrieved from (Original work published 2021)

Chicago Citation

Marrandino, Alessandro. (2021) 2021. Machine Learning with BigQuery ML. 1st ed. Packt Publishing.

Harvard Citation

Marrandino, A. (2021) Machine Learning with BigQuery ML. 1st edn. Packt Publishing. Available at: (Accessed: 15 October 2022).

MLA 7 Citation

Marrandino, Alessandro. Machine Learning with BigQuery ML. 1st ed. Packt Publishing, 2021. Web. 15 Oct. 2022.