Google is adding a pre-trained foundation model for tabular data to BigQuery, allowing enterprise teams to generate predictions from structured data without having to train and deploy a machine learning model from scratch.
The TabFM model, which has been developed by Google Research and is now available in preview, can perform classification and regression, two common types of predictive machine learning.
While classification is used to predict a category or outcome, for example, whether a customer is likely to churn or whether a transaction may be fraudulent, regression is typically used to predict a numerical value, such as the expected value of a customer or the amount of a future claim.
Traditionally, enterprise teams in a typical machine learning workflow would need to train and deploy a separate ML model for each use case they want to pursue. In contrast, TabFM uses historical data as in-context examples to make predictions, the company wrote in a blog post.
That means teams can provide labeled historical data, where the outcome is already known, along with the new data they want to make predictions about, and use BigQuery’s AI.PREDICT function to generate those predictions.
