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)
1
Section 1: Introduction and Environment Setup
5
Section 2: Deep Learning Networks
9
Section 3: Advanced Models with BigQuery ML
15
Section 4: Further Extending Your ML Capabilities with GCP

Exploring and understanding the dataset

Before diving into the machine learning implementation, we'll start analyzing the dataset that will be used to train our machine learning model.

For this use case, we'll use the BigQuery public dataset we've already used in Chapter 5, Predicting Boolean Values Using Binary Logistic Regression. This dataset contains information on taxi rides collected by the City of Chicago, which can be found at the following link: https://console.cloud.google.com/marketplace/details/city-of-chicago-public-data/chicago-taxi-trips.

Let's start by getting a clear understanding of the information that we have in our dataset to build our K-Means clustering model.

Understanding the data

In this section, we'll explore the structure of the data we'll use to develop our BigQuery ML model.

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

  1. Log in to GCP and access the BigQuery user interface from the navigation...