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

Discovering the BigQuery web UI

When the BigQuery APIs are enabled, in order to access the BigQuery UI, you can open the GCP navigation menu and select BigQuery from the list of Google Cloud services.

At first glance (see the following screenshot), the BigQuery UI might seem complex because it contains a lot of information and buttons. On the left side of the screen, we can see a column occupied by the navigation panel (1). This panel is split into two main sections. In the upper one, you can access the following:

  • Query history, which tracks all the queries previously executed with their execution statuses.
  • Saved queries, an area to store the queries that you save and use more frequently.
  • Job history, to keep track of all the bulk load, export, and copy operations with their execution statuses.
  • Transfers enables us to ingest data into BigQuery, leveraging BigQuery Data Transfer Service from Software as a Service (SaaS) applications, such as Google Analytics...