Bigquery ML Models
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 lets you create and execute machine learning models using GoogleSQL queries. BigQuery ML democratises machine learning by letting SQL practitioners build models using existing SQL tools and skills. BigQuery ML increases development speed by eliminating the need to move data.
A model in BigQuery ML represents what a machine learning (ML) system has learned from training data.
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.
Google BigQueryML supports two types of models:
- Linear Regression for continuous predictions (Forecasting)
- Logistic Regression for classification.
| ML Model Type | Data type Example |
|---|---|
Linear regression is for Forecasting. The output is numeric value (typically an integer or floating point) |
Forecast sales figures for next year given historical sales data. |
Logistic regression is for Classification. The output is 0 or 1 |
Classify an email as spam or not spam given the context. |
Note: There are many additional model types used in Machine Learning (like Neural Networks and decision trees) and available using libraries like TensorFlow.
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