You're reading from Machine Learning with BigQuery ML Create, execute, and improve machine learning models in BigQuery using standard SQL queries

Product type Paperback

Published in Jun 2021

Publisher Packt

ISBN-13 9781800560307

Length 344 pages

Edition 1st Edition

Languages

Python

Tools

BigQuery

Concepts

Machine Learning

Author (1):

Alessandro Marrandino

View More author details

Table of Contents (20) Chapters

Preface

1. Section 1: Introduction and Environment Setup

2. Chapter 1: Introduction to Google Cloud and BigQuery FREE CHAPTER

3. Chapter 2: Setting Up Your GCP and BigQuery Environment

4. Chapter 3: Introducing BigQuery Syntax

5. Section 2: Deep Learning Networks

6. Chapter 4: Predicting Numerical Values with Linear Regression

7. Chapter 5: Predicting Boolean Values Using Binary Logistic Regression

8. Chapter 6: Classifying Trees with Multiclass Logistic Regression

9. Section 3: Advanced Models with BigQuery ML

10. Chapter 7: Clustering Using the K-Means Algorithm

11. Chapter 8: Forecasting Using Time Series

12. Chapter 9: Suggesting the Right Product by Using Matrix Factorization

13. Chapter 10: Predicting Boolean Values Using XGBoost

14. Chapter 11: Implementing Deep Neural Networks

15. Section 4: Further Extending Your ML Capabilities with GCP

16. Chapter 12: Using BigQuery ML with AI Notebooks

17. Chapter 13: Running TensorFlow Models with BigQuery ML

18. Chapter 14: BigQuery ML Tips and Best Practices

19. Other Books You May Enjoy

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...