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

Discovering BigQuery SQL

BigQuery supports two different SQL dialects: standard SQL and legacy SQL. In this book, we'll use Standard SQL, but it could be useful to know what Legacy SQL is and how to enable it if you want to test queries coming from legacy applications.

As we have already mentioned, BigQuery was developed as an internal product within Google and was initially realized to process log records. The query engine Dremel was able to support a limited set of SQL operations that are now defined as Legacy SQL.

In the following screenshot, you can see how to change the SQL dialect:

Figure 3.3 – Screenshot of the Query Settings menu to change SQL dialect

By default, the BigQuery UI is configured to use Standard SQL, but you are allowed to change the SQL dialect by using the specific option located in the Query Settings of the BigQuery web interface, or by prefacing your queries with the #legacySQL keyword in the first line of your SQL...