Question

Your team trained a demand-forecasting model that must score a 40 GB CSV file dropped into Azure Blob Storage every night. The job has no latency requirement and results are written back to a data lake for downstream reporting. You want to minimize compute cost by only provisioning resources when the scoring job runs. Which Azure Machine Learning deployment approach best fits this workload?

Accepted Answer

Deploy a batch endpoint backed by a compute cluster that scales from zero, and invoke it on a nightly schedule.

Answer

Deploy a managed online endpoint with a single instance and autoscale rules based on request volume.

Answer

Deploy a managed online endpoint with two deployments and split traffic 50/50 for redundancy.

Answer

Deploy the model to a Kubernetes online endpoint kept always running to guarantee immediate scoring.