Question

A retail analytics team needs to score customer churn predictions for its entire 40-million-row customer base once every night. The results are written to a Delta table that a BI dashboard reads the next morning. There is no requirement for sub-second response times, and no external application calls the model interactively. Which deployment paradigm best fits this workload?

Accepted Answer

Batch inference using an MLflow model loaded as a Spark UDF over the full customer DataFrame

Answer

Real-time inference via a Databricks Model Serving endpoint queried per customer

Answer

Streaming inference with Structured Streaming reading from a continuous source

Answer

Real-time serving with an autoscaling endpoint behind a REST API gateway