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

Chapter 12: Using BigQuery ML with AI Notebooks

For data scientists and machine learning engineers, notebooks are a fundamental productivity tool. Notebooks allow us to interact with computing resources because we can use them to write and execute code, visualize the results, and share the outcomes with other data scientists. Data engineers, data scientists, and machine learning engineers make experiments and explore data before deploying code into the production environment. They leverage notebooks because they offer a flexible and agile environment to develop and test in.

In this chapter, we'll learn what AI Platform Notebooks is, how to provision a notebook environment, and how to use it to develop a BigQuery ML model.

We'll start by discovering the basics of notebooks and then start getting some hands-on practice with Google Cloud Platform (GCP).  

In this chapter, we'll cover the following topics:

  • Discovering AI Platform Notebooks
  • ...