You have a real-time inference web service which you have just deployed to Azure Kubernetes Service.
During its run, some unexpected errors occur.
You need to troubleshoot it quickly and cost-effectively.
Which is the quickest and cheapest option you should use?
Click on the arrows to vote for the correct answer
A. B. C. D.Answer: A.
Option A is CORRECT because using a local web service makes it easier to troubleshoot and debug problems.
If you have problems with your model deployed to ACI or AKS, try deploying it as a local web service.
You can then troubleshoot runtime issues by making changes to the scoring file that is referenced in the inference configuration, and reloading the service without redeploying it.
This can be done only with local services.
Option B is incorrect becauseAzure Container Instances is designed to be used for low-scale production deployments.
It is highly recommended to debug locally before deploying the web service.
Option C is incorrect because while an Azure compute instance might be a good target platform for debugging, using a local containerized environment is even better in this case.
Option D is incorrect because Azure Kubernetes Service is to be used for high-scale production deployments.
Being a powerful runtime environment, it is far too expensive for testing and debugging.
Reference:
Option B, deploying it to Azure Container Instances (ACI), is the quickest and cheapest option to troubleshoot a real-time inference web service that has just been deployed to Azure Kubernetes Service (AKS) and is experiencing unexpected errors.
Here's why:
ACI is a container-based solution for running Docker containers on Azure without the need to provision or manage virtual machines. It provides a lightweight and cost-effective option for running containerized applications on-demand, with pay-as-you-go pricing based on container usage.
Deploying the web service to ACI allows for easy debugging of the application because it provides an isolated and simplified environment for running the container. This means that any issues related to the underlying infrastructure can be quickly ruled out, and debugging can focus on the application code.
On the other hand, options A, C, and D are not as efficient as deploying to ACI for debugging purposes.
Option A, deploying it as a local web service and debugging locally, requires the developer to have a similar environment as the one on which the application is deployed. This can be time-consuming and not always feasible.
Option C, using a compute instance as a deployment target for debugging, can be costly and time-consuming to set up. Additionally, it may not provide an environment that accurately reflects the production environment, which could lead to difficulties in reproducing issues.
Option D, deploying it to AKS and setting the maximum number of replicas to one, could be costly because it requires running a full Kubernetes cluster with a single replica. Additionally, debugging in a production environment can be challenging and may introduce additional risk to the system's stability.
In summary, the quickest and cheapest option to troubleshoot a real-time inference web service that has just been deployed to AKS and is experiencing unexpected errors is to deploy it to ACI, which provides a lightweight, cost-effective, and simplified environment for running the container and focusing on debugging the application code.