Kubernetes training for customer proof-of-concept engineers

We offer private, instructor-led training for solutions engineers who turn customer concerns into Kubernetes evaluations and proofs of concept.

Your team knows the product's capabilities. A successful demonstration needs a clear technical question and representative conditions, rather than an assumption that a quiet lab proves production suitability.

LearnKube connects Kubernetes mechanisms to experiments, measurements, and explanations that help customers judge what an evaluation actually establishes.

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.

Make the evaluation answer a defined customer question

  • Design a bounded evaluation by translating a requirement into workload conditions and observations, so the customer knows what the demonstration will establish.
  • Validate expected behavior by examining resource, traffic, and recovery conditions, so the result reflects the agreed question rather than a convenient example.
  • Diagnose misleading results by separating configuration, capacity, and measurement effects, so engineers can explain an unexpected observation accurately.
  • Record acceptance and limits by documenting assumptions, evidence, and unresolved dependencies, so the customer can judge the next technical step.

A metric needs an operating question and a comparison

Kubernetes resource requests and limits influence scheduling and execution, while autoscaling responds to selected metrics. An evaluation must identify the workload and conditions behind those observations. Otherwise, a favorable graph can reflect different configuration or demand rather than the capability the customer wanted to investigate in the first place.

I ask what comparison makes the demonstration meaningful. If the workload, resource settings, or success criteria change between runs, the result needs that qualification before anyone presents it as evidence of a customer benefit.

— Daniele Polencic, LearnKube founder and Kubernetes instructor

Customer task or requirementKubernetes decisionPractice
Define an evaluation questionWorkload and operating conditionsWrite a bounded hypothesis
Select meaningful observationsMetrics and resource behaviorInspect the measurement path
Compare two configurationsControlled variables and capacityRun a repeatable comparison
Explain the resultAcceptance criteria and limitationsPresent evidence and open questions

Your team is not alone in technical customer evaluations

DoiT solutions engineers conduct customer discovery and prepare PerfectScale proofs of concept. They interpret Kubernetes metrics, tailor demonstrations, and guide customers from evaluation toward implementation.

For teams doing that work, we recommend a four-day workshop on technical reasoning, representative experiments, and clear interpretation of results. The focus is evaluating claims rather than promising a particular optimization outcome.

A recommended four-day workshop for solutions engineers

This proposed agenda carries a customer question through a reference workload. Engineers practice explaining the mechanism and the limits of each observation.

Day 1

We connect containers, Deployments, Services, probes, and releases to the intended demonstration. You will define what successful behavior means for the selected question.

Hands-on exercises

  • Turn a customer concern into a bounded workload experiment.
  • Distinguish rollout completion from the evaluation's application check.

Day 2

You will use Helm and compare Kustomize to control configuration between runs. We inspect controller and node responsibilities that can affect an observation.

Hands-on exercises

  • Record the configuration and assumptions used by the example.
  • Change one condition and explain the effect on workload behavior.

Day 3

We trace DNS, ingress, network policies, mesh use cases, and scheduling. The exercise examines whether the lab's traffic and capacity conditions reflect the customer question.

Hands-on exercises

  • Identify a network assumption that limits the demonstration.
  • Compare results when placement or available capacity changes.

Day 4

You will examine storage, secrets, HPA metrics, authentication, and RBAC as evaluation dependencies. We connect measurements to an evidence-based customer explanation.

Hands-on exercises

  • Compare resource demand with the metric used for scaling.
  • Present the result, its limitations, and the remaining implementation questions.

Your customer questions, evaluation workloads, and acceptance criteria can shape the agenda. Get in touch to tailor the workshop to your Kubernetes proofs of concept.

Challenge the interpretation before presenting the result

When an engineer attributes a faster response to a product change, the instructor can compare workload and resource conditions. The team can explain which variables support the claim and which still require investigation.

Asked what he liked most about the course, Armand wrote:

willingness to have Off-script conversations and whiteboarding

— Armand, Director of Solutions Engineering at F5.

Bring the customer question and the evidence it needs

Tell us which evaluations your team runs, which workload conditions matter, and how customers judge the result. We will recommend technical experiments and interpretation exercises for that work.

Discuss your team's training