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

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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

Summary

In this chapter, we learned what TensorFlow is and why it is so important for the ML industry.

First, we analyzed the main commonalities and differences between BigQuery ML and TensorFlow, and we understood that they are addressed to different target personas within the ML community.

Then, we discovered how we can complement BigQuery ML and TensorFlow to get the maximum value by combining these two frameworks.

By taking a gradual and step-by-step approach, we learned how to export BigQuery ML models into the TensorFlow format so that we can deploy them into environments other than BigQuery.

After that, we tested how to import and use a TensorFlow model in BigQuery ML. This approach enables data analysts to easily access and use advanced TensorFlow ML models that have been developed by data scientists and ML engineers. Finally, after importing the ML model, we tested the imported ML model on a BigQuery table to predict the trip duration of a bike ride with the New...