Which combination of practices and AWS services is MOST LIKELY to result in a maintainable, scalable, and cost-effective ML infrastructure?

You are a lead machine learning engineer at a growing tech startup that is developing a recommendation system for a mobile app. The recommendation engine must be able to scale quickly as the user base grows, remain cost-effective to align with the startup’s budget constraints, and be easy to maintain by a small team of engineers. The company has decided to use AWS for the ML infrastructure. Your goal is to design an infrastructure that meets these needs, ensuring that it can handle rapid scaling, remains within budget, and is simple to update and monitor.

Which combination of practices and AWS services is MOST LIKELY to result in a maintainable, scalable, and cost-effective ML infrastructure?
A . Implement Amazon SageMaker for model training, deploy the models using Amazon EC2 with manual scaling to handle inference, and use AWS CloudFormation for managing infrastructure as code to ensure repeatability
B . Use Amazon SageMaker for both training and deployment, leverage auto-scaling endpoints for real-time inference, and apply SageMaker Pipelines for orchestrating end-to-end ML workflows, ensuring scalability and automation
C . Use Amazon SageMaker for training, deploy models on Amazon ECS for flexible scaling, and implement infrastructure monitoring with a combination of CloudWatch and AWS Systems Manager to ensure maintainability
D . Train models using Amazon EMR for cost efficiency, deploy the models using AWS Lambda for serverless inference, and manually monitor the system using CloudWatch to reduce operational overhead

View Answer

Answer: B

Explanation:

Correct option:

Use Amazon SageMaker for both training and deployment, leverage auto-scaling endpoints for real-time inference, and apply SageMaker Pipelines for orchestrating end-to-end ML workflows, ensuring scalability and automation

Amazon SageMaker provides a managed service for both training and deployment, which simplifies the infrastructure and reduces operational overhead. Auto-scaling endpoints in SageMaker ensure the system can handle increasing demand without manual intervention. SageMaker Pipelines automates the entire ML workflow, enabling continuous integration and delivery (CI/CD) practices, making the infrastructure scalable, maintainable, and cost-effective.

Incorrect options:

Implement Amazon SageMaker for model training, deploy the models using Amazon EC2 with manual scaling to handle inference, and use AWS CloudFormation for managing infrastructure as code to ensure repeatability – Using Amazon SageMaker for training and Amazon EC2 for inference with manual scaling can work, but it requires more effort to manage scaling, and manually managing infrastructure is less maintainable. Auto-scaling and automation would be more effective for a growing startup.

Train models using Amazon EMR for cost efficiency, deploy the models using AWS Lambda for serverless inference, and manually monitor the system using CloudWatch to reduce operational overhead – While Amazon EMR is cost-effective for big data processing, it’s not optimized for ML model training in the same way that SageMaker is. AWS Lambda is useful for serverless inference but may not scale effectively for high-volume, real-time recommendations. Manual monitoring adds operational overhead.

Use Amazon SageMaker for training, deploy models on Amazon ECS for flexible scaling, and implement infrastructure monitoring with a combination of CloudWatch and AWS Systems Manager to ensure maintainability – Amazon ECS offers flexible scaling, but SageMaker’s auto-scaling capabilities and built-in integration with ML workflows make it more suitable for this use case. Additionally, SageMaker Pipelines offers better orchestration for ML tasks compared to a manually managed solution.

References:

https://docs.aws.amazon.com/sagemaker/latest/dg/endpoint-auto-scaling-prerequisites.html

https://docs.aws.amazon.com/sagemaker/latest/dg/pipelines.html