Learn what are compute targets in Azure Machine Learning, how they differ from each other, and how to choose the right one for your machine learning scenario.
Table of Contents
Question
You created a machine learning model and trained it. Now you want to run the model to predict data. Which compute target should you use?
A. Compute Clusters
B. Compute Instances
C. Inference Clusters
Answer
C. Inference Clusters
Explanation
Inference Clusters are used as deployment targets for predictive services that use your trained models.
The correct answer is C. Inference Clusters.
Inference clusters are compute targets that are used to run machine learning models to predict data. They are also known as Azure Machine Learning endpoints. Inference clusters are fully managed computes for real-time (managed online endpoints) or batch (managed batch endpoints) inference. They support GPU acceleration and autoscaling.
Compute clusters are compute targets that are used to train machine learning models on large datasets or perform distributed training. They are also known as Azure Machine Learning compute clusters. Compute clusters are single- or multi-node clusters that autoscale each time you submit a job.
Compute instances are compute targets that are used to develop and test machine learning models on a small amount of data. They are also known as Azure Machine Learning compute instances. Compute instances are cloud-based virtual machines that you can use as a workstation for data science tasks.
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