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 being a business analyst that works for the Google Merchandise e-commerce store. The website sells different Google-branded products to different users. Some of the users are registered and have their own identifier, and their clickstream activities are collected in a specific dataset.

Important note

Clickstream data is the digital footprint left by users navigating a specific website. This data typically includes the web pages they visited, the time they spent on each page, the device they used, the origin of the traffic, and other relevant information.

In this scenario, the data is collected by Google using Google Analytics 360 from the Google Merchandise e-commerce portal. This tool can be integrated with any website and allows us to gather information about the users' behavior on each page of the portal for further analysis and analytics.

The following screenshot is of the Google Merchandise Store, which sells Google-branded...