What is BQML?

Google Big Query Machine Learning

Big Query Machine Learning (BQML) enables users to create and execute machine learning models is Big Query using SQL queries. The goal is to democratize machine learning by enabling SQL practitioners to build models using their existing tools and to increase development to speed by eliminating the need for data movement.

BigQuery ML functionality is available by using:

• The Google Cloud Console
• The bq command-line tool
• The BigQuery REST API
• An external tool such as a Jupyter notebook or business intelligence platform

Machine learning on large datasets requires extensive programming and knowledge of ML frameworks. These requirements restrict solution development to a very small set of people within each company, and they exclude data analysts who understand the data but have limited machine learning knowledge and programming expertise.

BigQuery ML empowers data analysts to use machine learning through existing SQL tools and skills. Analysts can use BigQuery ML to build and evaluate ML models in BigQuery. Analysts don’t need to export small amounts of data to spreadsheets or other applications or wait for limited resources from a data science team.

There is a newly available e-commerce dataset that has millions of Google Analytics records for the Google Merchandise Store loaded in BigQuery.