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

Preparing the datasets

In this section, we'll learn about which techniques we can apply to ensure that the data we will use to build our ML model is correct and produces the desired results. After that, we'll discover the strategies that we can use to segment the datasets into training, validation, and test sets.

Working with high-quality data

In this section, we'll understand the characteristics that our datasets should have in order to develop effective BigQuery ML models.

Since ML models learn from data, it's very important to feed our ML algorithms with high-quality data, especially during the training phase. Since data quality is a very broad topic, it would require a specific book to analyze it in detail. For this reason, we will focus only on main data quality concepts in relation to the building of a ML model.

Important note

Data quality is a discipline that includes processes, professionals, technologies, and best practices to identify and...