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

Introducing the business scenario

Imagine being a business analyst who works for the state of Iowa. The state monitors the retail distribution of liquors and spirits by collecting data from every shop in the territory. Controlling liquor sales is particularly important for monitoring citizen health and for checking tax income.

In the following screenshot, you can see a picture of typical shelves of liquors and spirits in a shop:

Figure 8.1 – The shelves in a liquor shop

For our business scenario, we can imagine that the state of Iowa wants to predict the number of liters that will be sold in the first 30 days of 2020 leveraging the historical data collected in the previous years.

Your manager may ask you to predict the number of liters that will be sold by all the shops in the state by leveraging the time series data that was already collected in the database.

As a business analyst, your job is to find the best algorithm to forecast the sales...