Build your first machine learning model with Big Query ML

BigQuery ML allows the user to use regular SQL queries to develop and execute machine learning models in BigQuery. It democratises machine learning by enabling SQL practitioners to create models using their existing SQL tools and abilities. BigQuery ML accelerates development by removing the need to relocate data. Although it began with linear regression, it has now expanded to include more powerful models such as Deep Neural Networks and AutoML Tables by linking BigQuery ML to TensorFlow and Vertex AI as its backend. This article is focused on building a machine learning model with BigQuery ML. Following are the topics to be covered.

Table of contents

1. Getting started with Big Query ML
2. Authenticating and Project setup
3. Data extraction
4. Analysis of the data
5. Training and Evaluating the model

Machine learning on massive datasets necessitates substantial programming and understanding of ML frameworks, which not everyone possesses. BigQuery ML enables analysts to apply machine learning using their existing SQL tools and abilities. Let’s have a look at the supported ML models.

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