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


In this chapter, we've built our unsupervised machine learning model. After a brief introduction of the business scenario, we've discovered what unsupervised machine learning is and used the K-Means clustering algorithm to group similar observations within the same clusters.

Before diving into the development of the machine learning models, we applied some data quality checks to our dataset and selected the fields to use as features of our machine learning models.

During the training stage, we trained two different machine learning models to learn how to create a K-Means clustering model.

Then, we evaluated the two models, leveraging BigQuery ML SQL syntax and the functionalities available in the BigQuery UI.

In the last step, we tested our machine learning model to cluster the taxi drivers available in the dataset according to their features and into the clusters generated by the K-Means model.

Finally, we've also created a list of drivers belonging...