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Machine Learning with BigQuery ML
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.
What you will learn
Discover how to prepare datasets to build an effective ML model
Forecast business KPIs by leveraging various ML models and BigQuery ML
Build and train a recommendation engine to suggest the best products for your customers using BigQuery ML
Develop, train, and share a BigQuery ML model from previous parts with AI Platform Notebooks
Find out how to invoke a trained TensorFlow model directly from BigQuery
Get to grips with BigQuery ML best practices to maximize your ML performance
Who this book is for
This book is for data scientists, data analysts, data engineers, and anyone looking to get started with Google's BigQuery ML. You'll also find this book useful if you want to accelerate the development of ML models or if you are a business user who wants to apply ML in an easy way using SQL. Basic knowledge of BigQuery and SQL is required.
Table of Contents
Introduction to Google Cloud and BigQuery
Setting Up Your GCP and BigQuery Environment
Introducing BigQuery Syntax
Predicting Numerical Values with Linear Regression
Predicting Boolean Values Using Binary Logistic Regression
Classifying Trees with Multiclass Logistic Regression
Clustering Using the K-Means Algorithm
Forecasting Using Time Series
Suggesting the Right Product by Using Matrix Factorization
Predicting Boolean Values Using XGBoost
Implementing Deep Neural Networks
Using BigQuery ML with AI Notebooks
Running TensorFlow Models with BigQuery ML
BigQuery ML Tips and Best Practices
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.
What you will learn
Discover how to prepare datasets to build an effective ML model
Forecast business KPIs by leveraging various ML models and BigQuery ML
Build and train a recommendation engine to suggest the best products for your customers using BigQuery ML
Develop, train, and share a BigQuery ML model from previous parts with AI Platform Notebooks
Find out how to invoke a trained TensorFlow model directly from BigQuery
Get to grips with BigQuery ML best practices to maximize your ML performance
Who this book is for
This book is for data scientists, data analysts, data engineers, and anyone looking to get started with Google's BigQuery ML. You'll also find this book useful if you want to accelerate the development of ML models or if you are a business user who wants to apply ML in an easy way using SQL. Basic knowledge of BigQuery and SQL is required.
Table of Contents
Introduction to Google Cloud and BigQuery
Setting Up Your GCP and BigQuery Environment
Introducing BigQuery Syntax
Predicting Numerical Values with Linear Regression
Predicting Boolean Values Using Binary Logistic Regression
Classifying Trees with Multiclass Logistic Regression
Clustering Using the K-Means Algorithm
Forecasting Using Time Series
Suggesting the Right Product by Using Matrix Factorization
Predicting Boolean Values Using XGBoost
Implementing Deep Neural Networks
Using BigQuery ML with AI Notebooks
Running TensorFlow Models with BigQuery ML
BigQuery ML Tips and Best Practices
344 pages, ebook
Published June 11, 2021
5 people want to read
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