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

Creating your GCP account and project

The first step to start using GCP is the creation of a new GCP account and a new project. A project is a container of multiple GCP resources, and it is usually accessed by users through their accounts. Examples of GCP resources include Google Compute Engine VMs, Google Cloud Storage buckets, App Engine instances, and BigQuery datasets. A GCP project is also linked to a billing account to which all of the costs of services consumption are charged. GCP objects can be organized hierarchically. Projects are the first level of the hierarchy and can be grouped into folders. Each folder can have another folder or an organization node as a parent. The organization is at the top of the GCP hierarchy and cannot have a parent.

In the following diagram, you can see a hierarchy composed of an organization node, two main folders, and two nested folders linked to three different GCP projects:

Figure 2.1 – GCP resource hierarchy...