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

Exploring and understanding the dataset

In this section, we'll analyze and prepare the dataset for our use case. We'll start with some data quality checks, and then we'll segment the data into training, evaluation, and test tables.

Since the dataset has already been used in Chapter 6, Classifying Trees with Multiclass Logistic Regression, we will not start the analysis from the beginning. Instead, we'll focus on the most relevant queries for our business scenario.

Checking the data quality

To start our exploration of the data and to carry out data quality checks, we need to do the following:

  1. Log in to our Google Cloud Console and access the BigQuery User Interface (UI) from the navigation menu.
  2. Create a new dataset under the project that we created in Chapter 2, Setting Up Your GCP and BigQuery Environment. For this use case, we'll create a 10_nyc_trees_xgboost dataset with the default options.
  3. First of all, let's check if...