"Benefits of Migrating to AWS for Machine Learning"
Question
You are a consultant for a company developing a complex machine learning project relying on on-premises HPC frameworks with a plan to migrate to Amazon Web Services so they can leverage elastic and scalable cloud infrastructure and fast networking while keeping direct control over their computing infrastructure.
Which statement is correct in this sense?
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B. C. D. E.The correct answer for this question is option B: Think of combining AWS ParallelCluster and Amazon FSx file system.
Explanation:
Option A suggests combining Amazon SageMaker and Amazon FSx file system. While Amazon SageMaker is a powerful machine learning platform that provides a managed environment for building, training, and deploying machine learning models, Amazon FSx is a fully-managed file system that is optimized for enterprise HPC workloads. While this combination may be useful for certain use cases, it may not be the most appropriate for this scenario where the company wants to migrate its on-premises HPC framework to AWS while maintaining direct control over their computing infrastructure.
Option B suggests combining AWS ParallelCluster and Amazon FSx file system. AWS ParallelCluster is an AWS-supported open source cluster management tool that makes it easy for users to deploy and manage HPC clusters on AWS. It provides a simple interface for launching HPC clusters that are customized for specific applications, and it supports a range of popular job schedulers, including Slurm and Torque. When combined with Amazon FSx, users can run their HPC workloads on a file system that is optimized for high performance and low latency.
Option C suggests a customized setting of an HPC cluster running SageMaker algorithms. While this may be possible, it is not the most appropriate solution for this scenario where the company wants to maintain direct control over their computing infrastructure.
Option D is incorrect as it suggests that all HPC solutions on AWS are offered as a managed service. While AWS does offer managed HPC services, such as AWS ParallelCluster and Amazon FSx, users can also deploy and manage their own HPC clusters on EC2 instances using open source tools like Slurm and Torque.
Option E is incorrect as it suggests that SageMaker is a self-service solution to run distributed machine learning (ML) workloads. While SageMaker does provide a managed environment for building, training, and deploying machine learning models, it is not specifically designed for running distributed ML workloads. AWS provides a range of other services, such as Amazon EMR and AWS Batch, that are designed specifically for running distributed workloads on AWS.
