What should you do?

You use the Azure Machine Learning designer to create and run a training pipeline.

The pipeline must be run every night to inference predictions from a large volume of files.

The folder where the files will be stored is defined as a dataset.

You need to publish the pipeline as a REST service that can be used for the nightly inferencing run.

What should you do?
A . Create a batch inference pipeline
B. Set the compute target for the pipeline to an inference cluster
C. Create a real-time inference pipeline
D. Clone the pipeline

View Answer

Answer: A

Explanation:

Azure Machine Learning Batch Inference targets large inference jobs that are not time-sensitive. Batch Inference provides cost-effective inference compute scaling, with unparalleled throughput for asynchronous applications. It is optimized for high-throughput, fire-and-forget inference over large collections of data.

You can submit a batch inference job by pipeline_run, or through REST calls with a published pipeline.

Reference: https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/machine-learning-pipelines/parallel-run/README.md