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

Understanding feature engineering

In this section, we'll understand which techniques we can use to improve the features of a BigQuery ML model before the training stage.

Important note

Feature engineering is the practice of applying preprocessing functions on raw data, to extract features useful for training a ML model. Creating preprocessed features can significantly improve the performance of a ML model.

By design, BigQuery ML automatically applies feature engineering during the training phase when we use the CREATE MODEL function, but it also allows us to apply preprocessing transformations as well.

In order to automatically apply the feature engineering operations during the training and the prediction stage, we can include all the pre-processing functions into the TRANSFORM clause when we train the BigQuery ML model.

As we can see from the following code example, we need to use the TRANSFORM clause before the OPTIONS clause, and after the CREATE MODEL statement...