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

Exploring and understanding the dataset

Before starting the development of the machine learning model, we'll start looking at the dataset and its structure.

Let's start by getting a clear understanding of the time series data that we have in the BigQuery public dataset to build our forecasting model in the next section.

Understanding the data

In this section, we'll analyze the structure of the data to identify the fields to use for the machine learning model creation.

To start exploring the data, we need to do the following:

  1. Log into our Google Cloud Console and access the BigQuery user interface from the navigation menu.
  2. Create a new dataset under the project that we created in Chapter 2, Setting Up Your GCP and BigQuery Environment. For this use case, we'll create the dataset 08_sales_forecasting with the default options.
  3. Open the GCP project bigquery-public-data that hosts the BigQuery public datasets and browse the datasets until...