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

Introducing the business scenario

Imagine that you are a business analyst who works for large taxi companies in Chicago. These taxi companies make thousands of trips every day to satisfy the public transport needs of the entire city. The work and the behavior of the taxi drivers are fundamental in generating revenues for companies and delivering an effective service for all customers, every day.

For our business scenario, let's imagine that all the taxi companies want to give an additional reward to drivers who perform the best. The goal of the companies is to segment the drivers into three distinct categories, according to generated revenue and driving speed. The three groups can be described as follows:

  • The Top Drivers are the employees with the best revenue and efficiency throughout the year. This group will receive a huge additional reward.
  • The Good Drivers are drivers who performed well but aren't excelling. This group will not receive any reward.
  • ...