Book Image

Machine Learning with BigQuery ML

By : Alessandro Marrandino
Book Image

Machine Learning with BigQuery ML

By: Alessandro Marrandino

Overview of this book

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. 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.
Table of Contents (20 chapters)
Section 1: Introduction and Environment Setup
Section 2: Deep Learning Networks
Section 3: Advanced Models with BigQuery ML
Section 4: Further Extending Your ML Capabilities with GCP


Throughout this first chapter, we've taken the first steps into learning what GCP offers, how it is different from other public cloud providers, and how Google is building on its ubiquitous applications such as Gmail and Google Maps to provide great services to companies via GCP.

We've also discovered that Google's proven experience in AI and ML, developed through the making of products such as Google Photos, also forms part of the services of GCP. Each AI and ML service can address various use cases and different types of users according to their skills and background. For example, most technical users, such as data scientists, can leverage TensorFlow to have great flexibility and control over their developed ML models, while business users can use Google's solutions to solve specific challenges with Document AI and Contact Center AI. The intermediate category is composed of AI and ML building blocks; these services can accelerate the development of new ML use cases or spread the usage of innovative techniques through a company.

One of these building blocks is BigQuery: its extension, BigQueryML, enables the development of ML models by leveraging existing SQL skills. The use of BigQuery ML can bring great benefits to companies that want to democratize ML, enabling a large segment of employees to participate by simplifying the heaviest and most time-consuming activities that usually require the involvement of different stakeholders, skills, and tools.

In the next chapter, we will get hands-on by creating a new Google Cloud project and accessing BigQuery for the first time.