Unlock powerful insights and forecasts with Predictive AI Functions

Summary

Data teams frequently field questions about future sales, metric movements, and anomalous data, but answering them typically requires data science teams to build and manage models, creating a bottleneck. Predictive AI Functions, now in Beta, remove that bottleneck by providing SQL-native functions for forecasting, driver analysis, and anomaly detection, backed by state-of-the-art ML models maintained centrally by Databricks. The launch includes ai_forecast for time series projection, ai_top_drivers for segment driver analysis, ai_detect_anomalies for outlier detection, and ai_predict_value and ai_predict_class for regression and classification. These functions can be called directly in SQL, invoked through Genie with plain-language questions, or embedded in dashboards, delivering consistent, high-quality answers without infrastructure management. Benchmarking on fev-bench shows ai_forecast outperforms forecasting functions from leading cloud data warehouses, achieving industry-leading accuracy out of the box with no tuning required.

Takeaways

1. 1

   Predictive AI Functions include ai_forecast, ai_top_drivers, ai_detect_anomalies, ai_predict_value, and ai_predict_class, all callable as native SQL functions.

2. 2

   ai_forecast uses a transformer-based time series foundation model pretrained on diverse time series, requiring no tuning and supporting holiday effects and external covariates.

3. 3

   In fev-bench benchmarking, ai_forecast outperformed forecasting functions from a leading cloud data warehouse, achieving a higher task win rate.

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