A containerized inference service needs resource limits, health checks and predictable scaling behavior. Kubernetes coordinates workloads but does not make a model automatically reliable.
Before you begin
Know basic containers and use an authorized local or disposable training cluster. Do not experiment on an unapproved production workload.
Work through the idea
Start from one known working service and record its resource, configuration and health requirements. Test an intentional failure in a controlled environment and describe recovery. Keep credentials out of the manifest and repository. Distinguish what orchestration provides from what the application must implement itself.
- Package a known working service
- Define resource and health expectations
- Plan rollout and failure handling
Practice task
Design a fictional inference deployment and explain how an unhealthy instance is replaced.
Check your result
Include model-version visibility and the behavior of a request during rollout.
A mistake to avoid
Do not add orchestration before understanding the service's resource needs.
Keep a review note
Save the inputs or scenario, your reasoning and the result you checked. Explain one assumption and one case that needs more work. Revise the exercise after feedback and distinguish what you demonstrated from what remains untested.
Official reference for further study
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