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 built a recommendation engine based on the matrix factorization algorithm. After we introduced the business scenario, we discovered what matrix factorization is and the difference between explicit and implicit models. Before diving into data exploration, we enabled BigQuery Flex Slots, which are necessary to train this category of ML algorithms.

Then, we applied some analyses and data preparation steps to the sample data collected by Google from the Google Merchandise e-commerce portal. Here, we've focused on the fields that were actually required to build our BigQuery ML model.

Next, we created our training table, which includes the purchases that were made by each user, along with the related quantity for each product.

After that, we trained our matrix factorization model on the data that we'd prepared. When the model was trained, we evaluated its key performance indicators using SQL code and the BigQuery UI.

Finally, we generated...