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 8: Forecasting Using Time Series

Predicting future trends using historical data is one of the most fascinating activities that we can do with machine learning.

Making predictions based on historical data points and time series is particularly interesting and can be very useful in different industries. Forecasting can help us in predicting the future, but also in identifying anomalies in data that don't respect the expected pattern.

In this chapter, we'll focus on time series forecasting by using the ARIMA Plus algorithm. This technique can be used to predict numerical values in different fields, such as the sales of a company, the customers in a restaurant, stock prices, and the electricity consumption of a building.

To understand how we can use BigQuery ML to forecast trends and to effectively present our results, we'll go through the following topics:

  • Introducing the business scenario
  • Discovering time series forecasting
  • Exploring and...