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 implemented a XGBoost classification model. We've remembered the business scenario that was already used in Chapter 6, Classifying Trees with Multiclass Logistic Regression, based on the need to automatically classify New York City trees. After that, we've learned the basics of the XGBoost boosted tree classification model.

In order to build an effective model, we performed data quality checks and then segmented the dataset according to our needs into three tables: one to host training data, a second one for the evaluation stage, and a last one to apply our classification model.

During the training phase of the BigQuery ML model, we've constantly improved the performance of the ML model, using ROC AUC as a key performance indicator (KPI).

After that, we've evaluated the best ML model on a new set of records to avoid any overfitting, becoming more confident about the good quality of our XGBoost classification model.