Machine Learning with BigQuery ML: Create, execute, and improve machine learning models in BigQuery using standard SQL queries

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 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 Cover Title Page Copyright and Credits Contributors Table of Contents Preface Section 1: Introduction and Environment Setup Chapter 1: Introduction to Google Cloud and BigQuery Introducing Google Cloud Platform Interacting with GCP Discovering GCP's key differentiators Exploring AI and ML services on GCP Core platform services Building blocks Solutions Introducing BigQuery BigQuery architecture BigQuery's advantages over traditional data warehouses Interacting with BigQuery BigQuery data structures Discovering BigQuery ML BigQuery ML benefits BigQuery ML algorithms Understanding BigQuery pricing BigQuery pricing BigQuery ML pricing Free operations and free tiers Pricing calculator Summary Further resources Chapter 2: Setting Up Your GCP and BigQuery Environment Technical requirements Creating your GCP account and project Registering a GCP account Exploring Google Cloud Console Creating a GCP project Activating BigQuery Discovering the BigQuery web UI Exploring the BigQuery public datasets Searching for a public dataset Analyzing a table Summary Further reading Chapter 3: Introducing BigQuery Syntax Technical requirements Creating a BigQuery dataset Discovering BigQuery SQL CRUD operations Diving into BigQuery ML Summary Further resources Section 2: Deep Learning Networks Chapter 4: Predicting Numerical Values with Linear Regression Technical requirements Introducing the business scenario Discovering linear regression Exploring and understanding the dataset Understanding the data Checking the data's quality Segmenting the dataset Training the linear regression model Evaluating the linear regression model Utilizing the linear regression model Drawing business conclusions Summary Further reading Chapter 5: Predicting Boolean Values Using Binary Logistic Regression Technical requirements Introducing the business scenario Discovering binary logistic regression Exploring and understanding the dataset Understanding the data Segmenting 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 Understanding the data Checking the data quality Segmenting 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 Technical requirements Introducing the business scenario Discovering K-Means clustering Exploring and understanding the dataset Understanding the data Checking the data quality Creating the training datasets Training the K-Means clustering model Evaluating the K-Means clustering model Using the K-Means clustering model Drawing business conclusions Summary Further resources Chapter 8: Forecasting Using Time Series Technical requirements Introducing the business scenario Discovering time series forecasting Exploring and understanding the dataset Understanding the data Checking the data quality Creating the training 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 Understanding the data Creating the training 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 Checking the data quality Segmenting 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 Technical requirements Introducing the business scenario Discovering DNNs DNNs in BigQuery ML Preparing the dataset Training the DNN models Evaluating the DNN models Using the DNN models Drawing business conclusions Deep neural networks versus linear models Summary Further resources Section 4: Further Extending Your ML Capabilities with GCP Chapter 12: Using BigQuery ML with AI Notebooks Technical requirements Discovering AI Platform Notebooks AI Platform Notebooks pricing Configuring the first notebook Implementing BigQuery ML models within notebooks Compiling the AI notebook Running the code in the AI notebook Summary Further resources Chapter 13: Running TensorFlow Models with BigQuery ML Technical requirements Introducing TensorFlow Discovering the relationship between BigQuery ML and TensorFlow Understanding commonalities and differences Collaborating with BigQuery ML and TensorFlow Converting BigQuery ML models into TensorFlow Training the BigQuery ML to export it Exporting the BigQuery ML model Running TensorFlow models with BigQuery ML Summary Further resources Chapter 14: BigQuery ML Tips and Best Practices Choosing the right BigQuery ML algorithm Preparing the datasets Working with high-quality data Segmenting the datasets Understanding feature engineering Tuning hyperparameters Using BigQuery ML for online predictions Summary Further resources Other Books You May Enjoy Index **Manage different business scenarios with the right machine learning technique using Google's highly scalable BigQuery ML** * 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 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. * 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 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. 1. Introduction to Google Cloud and BigQuery 2. Setting Up Your GCP and BigQuery Environment 3. Introducing BigQuery Syntax 4. Predicting Numerical Values with Linear Regression 5. Predicting Boolean Values Using Binary Logistic Regression 6. Classifying Trees with Multiclass Logistic Regression 7. Clustering Using the K-Means Algorithm 8. Forecasting Using Time Series 9. Suggesting the Right Product by Using Matrix Factorization 10. Predicting Boolean Values Using XGBoost 11. Implementing Deep Neural Networks 12. Using BigQuery ML with AI Notebooks 13. Running TensorFlow Models with BigQuery ML 14. BigQuery ML Tips and Best Practices