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

Choosing the right BigQuery ML algorithm

In this section, we'll learn why it is so important to define a clear business objective before implementing a ML model, and we'll understand which BigQuery ML algorithm is suitable for each specific use case.

Important note

A data scientist is a professional in charge of the collection, analysis, and understanding of large amounts of data. This role typically requires a mix of skills, such as matching statistics and coding.

A data analyst is different from a data scientist. A data analyst is more focused on industry knowledge and business processes rather than on coding and programming skills. People in this role have huge experience in data manipulation and visualization and are able to present relevant business insights derived from data.

In order to get meaningful results in ML, it is necessary to define a clear business objective. Before starting on the actual implementation of the ML model, data analysts and data...