A container that starts on a laptop is not production-ready. Real environments expose weak defaults: oversized images, root processes, missing resource limits, unsafe secrets, unreliable probes, and deployments that fail when a node disappears.
Docker and Kubernetes are useful only when packaging and operations are engineered together. This guide covers the controls that make containerized workloads predictable, secure, and supportable under routine failures.
Build disciplined images
Use multi-stage builds, pinned base images, non-root users, and a minimal runtime surface. Keep configuration outside the image and exclude development tools and credentials. Scan dependencies and images in CI, but also define how findings are prioritized and patched.
Specify runtime behavior
Set CPU and memory requests and limits from observed usage. Implement readiness, liveness, and startup probes for distinct purposes. Handle termination signals, stop accepting work before shutdown, and allow in-flight requests or jobs to complete within a bounded period.
Make deployment failure-safe
Use rolling strategies, disruption budgets, anti-affinity, and autoscaling where workload behavior supports them. Validate manifests and policies before deployment. Ensure database migrations are compatible with old and new application versions rather than coupling schema changes to pod startup.
Prepare for operations
Centralize logs, metrics, traces, and audit events, then test alerts and runbooks. Restrict network paths and service-account permissions. Begin with a production-readiness review of one workload, including node loss and rollback exercises, before standardizing templates across the platform.