Question

Your team has a registered MLflow model that must score a nightly file drop of roughly 5 million records stored in a Blob datastore. Latency is not important, but the job must finish within a maintenance window and produce a single consolidated output file. You need to create the most cost-effective managed inference solution in Azure Machine Learning. What should you do?

Accepted Answer

Create a batch endpoint with a batch deployment, set mini_batch_size and instance_count to parallelize scoring, and configure output_action to append_row.

Answer

Deploy the model to a managed online endpoint and send the records in a loop of REST requests.

Answer

Deploy the model to a managed online endpoint with autoscaling enabled and increase the max instance count during the maintenance window.

Answer

Create a batch endpoint but set instance_count to 1 and output_action to summary_only to reduce compute usage.