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Kubernetes for ML: Deploying Models at Scale
Kubernetes runs copies of your model service, replaces the ones that die and swaps versions without downtime, if the manifest is right. This guide deploys a concrete-strength model to a local cluster, kills a pod, rolls out a new version under continuous traffic with no failed requests, and rolls back.
Docker and Kubernetes Interview Questions for ML
Container questions in ML interviews are about the mistakes that make images slow to build, huge, insecure or killed in production. Four are answered with real Docker builds and runs, from a cache that survives a code change to a container killed with exit code 137, then the Kubernetes questions that follow.