Discuss Amazon MLA-C01 Exam Topic 1 Question 12

An ML engineer needs to implement a solution to host a trained ML model. The rate of requests to the model will be inconsistent throughout the day.

The ML engineer needs a scalable solution that minimizes costs when the model is not in use. The solution also must maintain the model's capacity to respond to requests during times of peak usage.

Which solution will meet these requirements?

ACreate AWS Lambda functions that have fixed concurrency to host the model. Configure the Lambda functions to automatically scale based on the number of requests to the model.

BDeploy the model on an Amazon Elastic Container Service (Amazon ECS) cluster that uses AWS Fargate. Set a static number of tasks to handle requests during times of peak usage.

CDeploy the model to an Amazon SageMaker endpoint. Deploy multiple copies of the model to the endpoint. Create an Application Load Balancer to route traffic between the different copies of the model at the endpoint.

DDeploy the model to an Amazon SageMaker endpoint. Create SageMaker endpoint auto scaling policies that are based on Amazon CloudWatch metrics to adjust the number of instances dynamically.

Suggested Answer: D

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28 comments · newest first

Jina 7 months ago

I like D as well. CloudWatch metrics are reliable for scaling decisions.

Nieves 7 months ago

Option A seems good too, but fixed concurrency might limit flexibility.

Ty 7 months ago

Agreed! It saves costs when demand is low.

Chantay 8 months ago

I think option D is the best. Auto scaling is key for fluctuating requests.

Bambi 8 months ago

I agree, D is definitely the way to go for cost efficiency and responsiveness.

Emmett 8 months ago

Wait, can SageMaker really auto scale like that? Sounds too good to be true!

Aliza 8 months ago

B seems too rigid for fluctuating requests.

Tegan 9 months ago

I think A could work too, but fixed concurrency might limit flexibility.

Vallie 9 months ago

I'm a big fan of the serverless approach, but Option A with Lambda doesn't seem like the right fit here. The SageMaker auto-scaling in Option D is the way to go.

Barney 9 months ago

Haha, using Lambda functions with fixed concurrency? That's like trying to put a square peg in a round hole. Option D is clearly the way to go.

Novella 9 months ago

Option C with the Application Load Balancer could work, but it might be overkill for this use case. The SageMaker auto-scaling in Option D seems more straightforward.

Sina 9 months ago

I'm not sure about Option B. Manually setting the number of tasks on ECS seems like it might not be flexible enough to handle the changing demand.

Salena 9 months ago

I recall that using multiple copies of the model in option C could help with load balancing, but it might not be the most cost-efficient solution when the model is idle.

Luann 10 months ago

I feel like option D with SageMaker and auto-scaling makes the most sense since it can adjust based on demand, but I’m not entirely clear on how the scaling policies work.

William 10 months ago

I think deploying on Amazon ECS with Fargate could work, but it seems like having a static number of tasks might not be cost-effective during low usage times.

Javier 10 months ago

Option D sounds like the best choice for scalability!

King 10 months ago

Option D sounds like the best solution. Automatically scaling the SageMaker endpoint based on CloudWatch metrics seems like a great way to handle the inconsistent request rate.

Madelyn 11 months ago

I remember we discussed using AWS Lambda for handling variable request rates, but I'm not sure if fixed concurrency is the best approach for this scenario.

Martha 11 months ago

The SageMaker options (C and D) seem like the most straightforward way to address the requirements. I'm not as familiar with the Lambda and ECS solutions, so I'd want to do some more research on how they would work in this scenario.

Gwen 11 months ago

I'm leaning towards option D as well. The ability to dynamically adjust the number of instances based on usage patterns seems like it would meet the requirements of scalability and cost-effectiveness.

Sarah 12 months ago

Option C with the Application Load Balancer routing traffic between multiple copies of the model on a SageMaker endpoint also seems like a good choice. That way you can handle fluctuations in demand without over-provisioning resources.

Tonette 12 months ago

I'm a bit confused by the different options. Can someone explain the differences between the AWS Lambda and Amazon ECS solutions? I'm not sure I fully understand how they would work for this use case.

Craig 12 months ago

I think option D sounds like the best approach here. Deploying the model to a SageMaker endpoint and using auto-scaling policies based on CloudWatch metrics seems like a scalable and cost-effective solution.

Diego 7 months ago

↳ replying to Craig

Exactly! Flexibility is key for ML deployments.

Graham 7 months ago

↳ replying to Craig

Plus, SageMaker is built for ML models. Makes sense.

Alline 10 months ago

↳ replying to Craig

Yeah, it keeps costs low when usage is low. Smart choice!

Dominque 11 months ago

↳ replying to Craig

I agree, option D is definitely the way to go. Auto-scaling is crucial.

Paz 11 months ago

↳ replying to Craig

I like how it can handle peak loads efficiently too.

Amazon MLA-C01 Exam - Topic 1 Question 12 Discussion