Which of the following strategies should you consider to balance the tradeoffs between performance, cost, and latency when deploying your model in Amazon SageMaker?

You are an ML Engineer working for a logistics company that uses multiple machine learning models to optimize delivery routes in real-time. Each model needs to process data quickly to provide up-to-the-minute route adjustments, but the company also has strict cost constraints. You need to deploy the models in an environment where performance, cost, and latency are carefully balanced. There may be slight variations in the access frequency of the models. Any excessive costs could impact the project’s

profitability.

Which of the following strategies should you consider to balance the tradeoffs between performance, cost, and latency when deploying your model in Amazon SageMaker? (Select two)
A . Choose a lower-cost CPU instance, accepting longer inference times, as the savings on compute costs are more important than minimizing latency
B . Leverage Amazon SageMaker Neo to compile the model for optimized deployment on edge devices, reducing latency and cost but with limited scalability for large datasets
C . Use Amazon SageMaker’s multi-model endpoint to deploy multiple models on a single instance, reducing costs by sharing resources
D . Implement auto-scaling on a fleet of medium-sized instances, allowing the system to adjust resources based on real-time demand, balancing cost and performance dynamically
E . Deploy the model on a high-performance GPU instance to minimize latency, regardless of the
higher cost, ensuring real-time route adjustments

View Answer

Answer: C, D

Explanation:

Correct options:

Use Amazon SageMaker’s multi-model endpoint to deploy multiple models on a single instance, reducing costs by sharing resources

Amazon SageMaker’s multi-model endpoint allows you to deploy multiple models on a single instance. This can significantly reduce costs by sharing resources among models, but it may introduce slight increases in latency due to the need to load the correct model into memory. This tradeoff can be acceptable if cost savings are a priority and latency requirements are not ultra-strict.

via – https://docs.aws.amazon.com/sagemaker/latest/dg/multi-model-endpoints.html

Implement auto-scaling on a fleet of medium-sized instances, allowing the system to adjust resources based on real-time demand, balancing cost and performance dynamically

Auto-scaling allows you to dynamically adjust the number of instances based on demand, which helps balance performance and cost. During peak times, more instances can be provisioned to maintain low latency, while during off-peak times, fewer instances are used, reducing costs. This strategy offers a flexible way to manage the tradeoffs between performance, cost, and latency.

Incorrect options:

Deploy the model on a high-performance GPU instance to minimize latency, regardless of the higher cost, ensuring real-time route adjustments – While deploying on a high-performance GPU instance would minimize latency, it may not be cost-effective, especially if the model does not require the full computational power of a GPU. The high cost might outweigh the benefits of lower latency.

Choose a lower-cost CPU instance, accepting longer inference times, as the savings on compute costs are more important than minimizing latency – Choosing a lower-cost CPU instance could lead to unacceptable delays in route adjustments, which could impact delivery times. In this scenario, optimizing latency is critical, and sacrificing performance for cost could be detrimental to the business.

Leverage Amazon SageMaker Neo to compile the model for optimized deployment on edge devices, reducing latency and cost but with limited scalability for large datasets – While Amazon SageMaker Neo can optimize models for deployment on edge devices, it is not the best fit for this scenario. Neo is more

suitable for low-latency, cost-effective deployments on devices with limited resources. In this scenario, the need for scalable, cloud-based infrastructure is more important.

References:

https://docs.aws.amazon.com/sagemaker/latest/dg/multi-model-endpoints.html

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