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 the relationship between BigQuery ML and TensorFlow

In this section, we'll understand the relationship between BigQuery ML and TensorFlow. After completing this section, we'll be able to understand when to use BigQuery ML and TensorFlow according to our use case, but also how to get the best out of the two technologies, when they're used together.

Understanding commonalities and differences

BigQuery ML and TensorFlow have some similar aspects, but there are also some relevant differences to highlight.

In the following table, we have summarized the main similarities and differences of these two frameworks:

Figure 13.1 – Comparing BigQuery ML and TensorFlow

First, it is important to underline that TensorFlow offers greater flexibility in terms of the ML models that can be implemented. While BigQuery ML is characterized by a specific list of supported model types (