Book description
Manage different business scenarios with the right machine learning technique using Google's highly scalable BigQuery ML
Key Features
- Gain a clear understanding of AI and machine learning services on GCP, learn when to use these, and find out how to integrate them with BigQuery ML
- Leverage SQL syntax to train, evaluate, test, and use ML models
- Discover how BigQuery works and understand the capabilities of BigQuery ML using examples
Book Description
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
- Machine Learning with BigQuery ML
- Contributors
- About the author
- About the reviewers
- Preface
- Section 1: Introduction and Environment Setup
- Chapter 1: Introduction to Google Cloud and BigQuery
- Chapter 2: Setting Up Your GCP and BigQuery Environment
- Chapter 3: Introducing BigQuery Syntax
- Section 2: Deep Learning Networks
- Chapter 4: Predicting Numerical Values with Linear Regression
-
Chapter 5: Predicting Boolean Values Using Binary Logistic Regression
- Technical requirements
- Introducing the business scenario
- Discovering binary logistic regression
- Exploring and understanding the dataset
- Training the binary logistic regression model
- Evaluating the binary logistic regression model
- Using the binary logistic regression model
- Drawing business conclusions
- Summary
- Further resources
-
Chapter 6: Classifying Trees with Multiclass Logistic Regression
- Technical requirements
- Introducing the business scenario
- Discovering multiclass logistic regression
- Exploring and understanding the dataset
- Training the multiclass logistic regression model
- Evaluating the multiclass logistic regression model
- Using the multiclass logistic regression model
- Drawing business conclusions
- Summary
- Further resources
- Section 3: Advanced Models with BigQuery ML
- Chapter 7: Clustering Using the K-Means Algorithm
-
Chapter 8: Forecasting Using Time Series
- Technical requirements
- Introducing the business scenario
- Discovering time series forecasting
- Exploring and understanding the dataset
- Training the time series forecasting model
- Evaluating the time series forecasting model
- Using the time series forecasting model
- Presenting the forecast
- Summary
- Further resources
-
Chapter 9: Suggesting the Right Product by Using Matrix Factorization
- Technical requirements
- Introducing the business scenario
- Discovering matrix factorization
- Configuring BigQuery Flex Slots
- Exploring and preparing the dataset
- Training the matrix factorization model
- Evaluating the matrix factorization model
- Using the matrix factorization model
- Drawing business conclusions
- Summary
- Further resources
-
Chapter 10: Predicting Boolean Values Using XGBoost
- Technical requirements
- Introducing the business scenario
- Discovering the XGBoost Boosted Tree classification model
- Exploring and understanding the dataset
- Training the XGBoost classification model
- Evaluating the XGBoost classification model
- Using the XGBoost classification model
- Drawing business conclusions
- Summary
- Further resources
- Chapter 11: Implementing Deep Neural Networks
- Section 4: Further Extending Your ML Capabilities with GCP
- Chapter 12: Using BigQuery ML with AI Notebooks
- Chapter 13: Running TensorFlow Models with BigQuery ML
- Chapter 14: BigQuery ML Tips and Best Practices
- Other Books You May Enjoy
Product information
- Title: Machine Learning with BigQuery ML
- Author(s):
- Release date: June 2021
- Publisher(s): Packt Publishing
- ISBN: 9781800560307
You might also like
book
Machine Learning with Core ML
Leverage the power of Apple's Core ML to create smart iOS apps About This Book Explore …
book
Machine Learning with TensorFlow
Machine Learning with TensorFlow gives readers a solid foundation in machine-learning concepts plus hands-on experience coding …
book
Interpretable Machine Learning with Python
A deep and detailed dive into the key aspects and challenges of machine learning interpretability, complete …
book
Hands-On Machine Learning with ML.NET
Create, train, and evaluate various machine learning models such as regression, classification, and clustering using ML.NET, …