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:
When unexpected errors occur in a real-time inference web service deployed on Azure Kubernetes Service, you need to troubleshoot the issue quickly and cost-effectively. Here's a detailed explanation of the options provided:
A. Deploy it as a local web service and debug locally Deploying the web service locally and debugging it locally can be a quick way to troubleshoot issues. However, it may not be the cheapest option since you may need to provision and manage the necessary infrastructure for the local deployment. Additionally, the local environment may not necessarily replicate the production environment, which can make it difficult to reproduce and solve the issue.
B. Deploy it to ACI Deploying the web service to Azure Container Instances (ACI) can be a quick and cost-effective option for troubleshooting. ACI is a serverless container solution that enables you to run containers in Azure without managing the underlying infrastructure. You can deploy the web service to ACI and access it using its public IP address. Since you only pay for the compute resources used by ACI, it can be a cost-effective option for short-term debugging.
C. Use a compute instance as deployment target for debugging Using a compute instance as a deployment target for debugging can be a quick and cost-effective option. You can provision a compute instance on Azure and deploy the web service to it. Since you have full control over the instance, you can customize it to match the production environment, which can help you to reproduce and solve the issue. Additionally, you only pay for the compute resources used by the instance, which can be cost-effective for short-term debugging.
D. Deploy it to AKS and set the maximum number of replicas to one; debug it in the production environment. Deploying the web service to AKS and setting the maximum number of replicas to one can help you to debug the issue in the production environment. However, this may not be the quickest or cheapest option since you may need to provision and manage the necessary infrastructure for AKS. Additionally, deploying the web service to the production environment can carry risks such as downtime or data loss if not done properly.
Overall, the quickest and cheapest option for troubleshooting a real-time inference web service on Azure Kubernetes Service is to deploy it to Azure Container Instances (ACI).