Kubernetes training for container-image customer support engineers

We offer private, instructor-led training for product support engineers who help customers use container images in Kubernetes workloads.

Your team knows the image and its intended contents. The customer's runtime settings change how it executes, including startup commands, identity, mounts, probes, and available debugging tools.

LearnKube uses controlled image and workload variations to separate product contents from environment assumptions and produce an actionable customer explanation.

Discuss your team's training

Hands-on learning and the skills engineers take back to work.

Preview: course-wide figures are not yet available.

  • Hands-on learning
    60%
    Of instruction time spent on labs and challenges.
  • Troubleshooting confidence
    86%
    Of respondents report greater confidence diagnosing Kubernetes problems.
  • Relevant to your work
    92%
    Of respondents say the course addressed their engineering responsibilities.
  • Skills put into practice
    95%
    Of respondents applied their new skills at work within 90 days.

Investigate the image in its actual execution conditions

  • Reproduce the workload behavior by identifying the image and runtime settings, so the investigation reflects the customer's application rather than only a local container command.
  • Preserve intended constraints by examining permissions and approved diagnostic methods, so investigation does not rely on weakening the customer's runtime controls.
  • Distinguish image and platform failures by inspecting startup, mounts, probes, and events, so the team can identify a missing dependency or incompatible assumption.
  • Prepare a useful correction or escalation by recording the relevant execution conditions, so the customer or product engineer can apply the next step.

The workload definition is part of the image's runtime environment

An image can behave differently under a customer's command, mounts, permissions, and probes. Minimal images also require diagnostic methods that do not assume a shell exists. Kubernetes supports ephemeral debug containers, subject to appropriate access and runtime support. Engineers must preserve relevant execution conditions while determining whether the failure belongs to image contents or workload configuration.

I ask which runtime assumption changed before calling the image defective. A missing shell explains why one debugging command fails, but it does not explain why the customer application fails to start or serve requests.

— Daniele Polencic, LearnKube founder and Kubernetes instructor

Customer task or requirementKubernetes decisionPractice
Reproduce startup behaviorImage, command, and configurationCompare execution settings
Inspect a minimal imagePermitted diagnostic methodsSelect a supported observation
Verify runtime compatibilityPermissions, mounts, and probesIsolate a changed assumption
Escalate an image issueReproduction and product boundariesRecord an actionable case

Your team is not alone in customer image support

Chainguard support engineers help enterprise customers with container-image issues using Linux, Docker, Kubernetes, and related tooling. They investigate failures, communicate with customers, and collaborate with product engineering.

For engineers working at that boundary, we recommend a four-day workshop that connects image behavior to Kubernetes execution conditions and permitted diagnosis.

A recommended four-day workshop for image-support teams

This proposed agenda uses representative images and workload configurations. It develops Kubernetes diagnostic reasoning alongside, rather than in place of, product-specific expertise.

Day 1

We connect images, commands, environment values, Pods, controllers, and probes. You will compare a container command with the conditions applied by a Kubernetes workload.

Hands-on exercises

  • Reproduce a startup failure under a specified configuration.
  • Distinguish a missing runtime dependency from a failed readiness check.

Day 2

You will inspect Helm values and Kustomize output alongside controller and node responsibilities. We trace how configuration changes the application environment without changing the image itself.

Hands-on exercises

  • Compare two rendered configurations for the same image.
  • Identify the setting that changes the observed behavior.

Day 3

We examine DNS, Services, ingress, network policies, mesh interactions, and placement. Diagnostic examples respect the customer's available tools and permissions.

Hands-on exercises

  • Investigate a minimal-image workload using an appropriate observation method.
  • Trace a connection failure that is independent of image contents.

Day 4

You will examine storage mounts, secrets, resource metrics, authentication, and RBAC. We connect those settings to a bounded reproduction and a clear customer recommendation.

Hands-on exercises

  • Isolate a filesystem or permission assumption behind the failure.
  • Prepare a report identifying the image, configuration, observations, and next owner.

Your images, customer runtime constraints, and support responsibilities can shape the agenda. Get in touch to tailor the workshop to your image-adoption support work.

Separate a failed debugging method from an application defect

When an engineer treats the absence of a shell as the application problem, the instructor can choose a permitted diagnostic approach. The team can inspect the actual startup behavior without confusing tooling with the customer's failure.

Cassandra described her course experience:

It is very informative. Everything I need to learn Kubernetes.

— Cassandra, F5.

Bring the image issues and customer execution conditions

Tell us which images your team supports, how customers run them, and which investigation methods are permitted. We will recommend workload comparisons and diagnostic exercises for that context.

Discuss your team's training