Which of the following strategies should you implement to effectively provision compute resources for both the production environment and the test environment using Amazon SageMaker, considering the different requirements for each environment?

You are an ML Engineer at a financial services company tasked with deploying a machine learning model for real-time fraud detection in production. The model requires low-latency inference to ensure that fraudulent transactions are flagged immediately. However, you also need to conduct extensive testing and experimentation in a separate environment to fine-tune the model and validate its performance before deploying it. You must provision compute resources that are appropriate for both environments, balancing performance, cost, and the specific needs of testing and production.

Which of the following strategies should you implement to effectively provision compute resources for both the production environment and the test environment using Amazon SageMaker, considering the different requirements for each environment? (Select two)
A . Leverage AWS Inferentia accelerators in the production environment to meet high throughput and low latency requirements
B . Provision CPU-based instances in both production and test environments to reduce costs, as CPU instances are generally cheaper than GPU instances
C . Use GPU-based instances in both production and test environments to ensure that the model inference and testing are both performed at maximum speed, regardless of cost
D . Use CPU-based instances in the test environment to save on costs during experimentation
E . Provision identical instances in both production and test environments to ensure consistent
performance between the two, eliminating the risk of discrepancies during deployment

View Answer

Answer: A, D

Explanation:

Correct options:

Use CPU-based instances in the test environment to save on costs during experimentation

For the test environment, CPU-based instances can be used to run experiments and validate the model, which helps reduce costs without compromising the ability to test different configurations and models.

Leverage AWS Inferentia accelerators in the production environment to meet high throughput and low latency requirements

AWS Inferentia accelerators are designed by AWS to deliver high performance at the lowest cost in Amazon EC2 for your deep learning (DL) and generative AI inference applications. The first-generation AWS Inferentia accelerator powers Amazon Elastic Compute Cloud (Amazon EC2) Inf1 instances that deliver higher throughput and lower latency than comparable Amazon EC2 instances. They also offer up to 70% lower cost per inference than comparable Amazon EC2 instances. Therefore, you can meet performance requirements in the production environment while optimizing costs.

Incorrect options:

Use GPU-based instances in both production and test environments to ensure that the model inference and testing are both performed at maximum speed, regardless of cost – While using GPU-based instances in both environments ensures high performance, it is not cost-effective. The test environment does not typically require the same level of performance as production, making GPU instances unnecessary and expensive.

Provision CPU-based instances in both production and test environments to reduce costs, as CPU instances are generally cheaper than GPU instances – Provisioning only CPU-based instances in both environments might save costs but would likely fail to meet the low-latency requirements in production. Inference times could be unacceptably slow, which is critical for real-time fraud detection.

Provision identical instances in both production and test environments to ensure consistent performance between the two, eliminating the risk of discrepancies during deployment – Although using identical instances in both environments ensures consistency, it is not cost-efficient. The test environment does not need to replicate the full performance of the production environment, so using less powerful and less expensive instances is more appropriate for testing purposes.

Reference: https://aws.amazon.com/machine-learning/inferentia/