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

Summary

In this chapter, we've built our first multiclass classification model. After a brief introduction to the use case, we discovered what multiclass logistic regression is and how it can be used to classify events, behaviors, and objects according to their features into more than two categories.

Before diving into the development of the ML model, we analyzed the schema of the dataset related to the trees in New York City and applied some data quality checks necessary to build an effective ML model.

During the training stage, we trained three different ML models using different features to gradually improve the performance of the BigQuery ML model.

Then, we chose the third ML model and we evaluated it against the evaluation dataset. In this phase, we noticed that the ML model was able to maintain its performance on new records also and was ready to pass to the next phase.

In the last step, we used our ML model to classify the trees in New York City into five different...