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)
1
Section 1: Introduction and Environment Setup
5
Section 2: Deep Learning Networks
9
Section 3: Advanced Models with BigQuery ML
15
Section 4: Further Extending Your ML Capabilities with GCP

Summary

In this chapter, we've learned the most important tips and best practices that we can apply during the implementation of a ML use case with BigQuery ML.

We've analyzed the importance of data preparation; we started looking at the data quality aspects; then, we've learned how we can easily split the data to get balanced training, validation, and test sets.

We then looked at how we can further improve a ML model's performance using BigQuery ML functions for feature engineering.

After that, we focused our attention on tuning hyperparameters. When we train a model, BigQuery ML allows us to choose different parameters, and these variables influence the training stage.

Finally, we have understood why it is so important to deploy BigQuery ML models on other platforms so that we get online predictions and satisfy near-real-time business scenarios.

Congratulations on finishing reading the book! You should now be able to use BigQuery ML for your business...