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

Evaluating the time series forecasting model

In this section, we'll evaluate the performance of the machine learning model that we trained in the previous one.

The evaluation stage of a time series model can be performed by using the ML.EVALUATE BigQuery ML function.

Let's execute the following query to extract all the evaluation parameters that characterize the ARIMA model:

 ML.EVALUATE(MODEL `08_sales_forecasting.liquor_forecasting`);

The results of the query are visualized in the following screenshot:

Figure 8.13 – The records extracted from the evaluation of the time series forecasting model

Each row defines each non-seasonal ARIMA model classified as an ARIMA(p,d,q) model. For each row, we can notice the following:

  • The field non_seasonal_p represents the parameter p of the ARIMA model. The value of the row is the number of autoregressive terms used for the prediction. It indicates the number of observations...