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

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