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

Running TensorFlow models with BigQuery ML

In this section, we'll import the TensorFlow model that we exported in the Converting BigQuery ML models into TensorFlow section. Once the model has been imported, we'll use it through the BigQuery ML syntax.

First, we need to remember that our BigQuery ML model has been exported into the folder of a Google Cloud storage bucket. The ML model is stored in the TensorFlow SavedModel format and is in the same format as any other ML model that's been developed by a data scientist using TensorFlow.

If we want to use a TensorFlow model in BigQuery ML, we need to perform the following steps:

  1. First, let's run the CREATE OR REPLACE MODEL SQL statement. Keep in mind that the path of the bucket –  'gs://<PROJECT_NAME>-us-bigqueryml-export-tf/bqml_exported_model/*' – is based on the name of your current project, so you need to replace the <PROJECT_NAME> placeholder with the...