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

Chapter 14: BigQuery ML Tips and Best Practices

BigQuery ML has the great advantage of democratizing the use of Machine Learning (ML) for data and business analysts. In fact, BigQuery ML enables users without any programming experience to implement advanced ML algorithms. Even though BigQuery ML is designed to simplify and automatize the creation of a ML model, there are some best practices and tips that should be adopted during the development life cycle of a ML algorithm to obtain an effective performance from it.

Having a background in data science can help us in further improving the performance of our ML models and in avoiding pitfalls during the implementation of a use case. In this chapter, we'll learn how to choose the right technique for each specific business scenario and will learn about the tools we can leverage to improve the performance of ML models.

Following a typical ML development life cycle, we'll go through the following topics:

  • Choosing...