Learn about the different types of compute resources that you can create in Azure Machine Learning Studio, such as compute clusters, inference clusters, and compute instances, and how they can help you train and deploy your machine learning models.
Table of Contents
Question
What type of compute resources can be created in Azure Machine Learning Studio?
A. Spot clusters
B. Compute clusters
C. Inference clusters
D. Compute instances
Answer
B. Compute clusters
C. Inference clusters
D. Compute instances
Explanation
The four types of compute resources available in Azure Machine Learning Studio are: Compute instances, Compute Clusters, Inference clusters and Attached Compute.
The correct answer is B, C, and D. Azure Machine Learning Studio allows you to create three types of compute resources: compute clusters, inference clusters, and compute instances.
- Compute clusters are scalable clusters of virtual machines that can run your training scripts or host your machine learning pipelines. You can configure the size, number, and type of VMs in a compute cluster, and it will automatically scale up or down based on the demand. Compute clusters can also use low-priority VMs to reduce the cost of training.
- Inference clusters are clusters of virtual machines that are used to deploy your trained models as web services. You can choose from different deployment options, such as Azure Container Instances (ACI) or Azure Kubernetes Service (AKS), depending on your scalability and security requirements. Inference clusters can also use autoscaling and load balancing to handle varying traffic and workload.
- Compute instances are fully managed cloud-based workstations that you can use to work with data and models in Azure Machine Learning Studio. You can use compute instances to run notebooks, scripts, or visual interface experiments, as well as to access data and compute resources in your workspace. Compute instances can also be integrated with VS Code extension, GitHub, or Azure DevOps for a seamless development experience.
Spot clusters are not a type of compute resource that can be created in Azure Machine Learning Studio. Spot clusters are a feature of Azure Batch that allows you to run large-scale parallel and high-performance computing (HPC) applications using low-priority VMs at a reduced cost. Spot clusters are not directly supported by Azure Machine Learning, but you can use Azure Batch as an attached compute target for your training jobs.
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