Google BigQuery ML Extends the Power of (Some) Modeling to Data Analysts

Last week at Google Next ‘18, Google announced a new beta capability in their BigQuery cloud data warehouse: BigQuery ML, which lets data analysts apply simple machine learning models to data residing in BigQuery data warehouses.

Data analysts know databases and SQL, but generally don’t have a lot of experience in building machine learning models using Python or R. An additional issue is the expense, time-consumption, and possible regulatory violations of moving data out of storage in order to send it through machine learning models. BigQuery ML aims to address these problems by letting data analysts push data through linear regression models (to predict a numeric value) or binary logistic regression models (to classify a value into one of two categories, such as “high” or “low”), using simple extensions of SQL on Google databases, run in place.

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