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

Introducing TensorFlow

In this section, we'll introduce TensorFlow, its origins, and what this framework has achieved in the ML community.

TensorFlow is an open source library that's used to develop ML models. It's very flexible and can be used to address a wide variety of use cases and business scenarios. Since TensorFlow is based on math functions, its name comes from the mathematical concept of the Tensor.

In math, a Tensor is an algebraic object that describes a relationship between sets of other algebraic objects. Some examples of tensors are vectors and matrixes.

The TensorFlow library was originally created by Google's engineers and then released under the Apache License in 2015. Now, it is recognized as one of the most popular ML frameworks due to its potential and flexibility. In fact, a TensorFlow model can be executed on local machines, on-premises servers, in the cloud, or at the edge, such as on mobile phones and video surveillance cameras...