Kubernetes training for customer AI infrastructure architects

We offer private, instructor-led training for customer-facing architects and technical specialists who translate AI workloads into Kubernetes infrastructure requirements.

Your team understands the customer's intended workload. Hardware capacity alone does not establish execution readiness, because placement, device access, images, data, and operating responsibilities must align.

LearnKube connects those dependencies through a representative workload and acceptance question, so engineers can explain which requirements belong to Kubernetes and which need specialist input.

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.

Turn an AI workload request into verifiable infrastructure requirements

  • Define the Kubernetes requirements by connecting workload lifecycle to resources, placement, and data access, so the customer can see the dependencies behind the proposal.
  • Validate a bounded readiness condition by inspecting execution and required inputs, so allocated hardware is not mistaken for a working application.
  • Diagnose blocked progress by separating scheduling, runtime, storage, and network evidence, so the team can identify the relevant specialist or platform owner.
  • Record acceptance and ownership by documenting what was verified and what remains outside scope, so customer and delivery teams share the same technical boundary.

Scheduling the hardware is only one execution requirement

Kubernetes GPU scheduling connects device resources to Pod requirements, while volume binding can add placement constraints of its own. An AI workload still needs compatible software and accessible data. Customer-facing specialists must connect these relationships to acceptance criteria rather than treat a scheduled Pod as proof that the workload can make useful progress.

I ask what the workload must demonstrate after it receives a device. A scheduled Pod does not prove that the framework can use the hardware or read its data, so the acceptance question needs both.

— Daniele Polencic, LearnKube founder and Kubernetes instructor

Customer task or requirementKubernetes decisionPractice
Specify execution capacityDevice and resource requirementsInspect a workload request
Place the workloadNode and storage constraintsDiagnose a Pending Pod
Establish useful executionRuntime and data dependenciesVerify a bounded application result
Define acceptance ownershipPlatform and specialist boundariesRecord evidence and open requirements

Your team is not alone in customer AI infrastructure work

Vultr works with customers on GPU workload patterns, cluster topology, storage, networking, operational readiness, and acceptance criteria. Kubernetes and Slurm are part of that infrastructure context.

For customer-facing specialists defining those requirements, we recommend a four-day workshop focused on Kubernetes reasoning and verification. Model training, specialist fabrics, and other schedulers remain separate areas of expertise.

A recommended four-day workshop for infrastructure requirement discussions

This proposed agenda uses a representative workload and available lab resources. GPU-specific exercises depend on an agreed environment; the core focus remains Kubernetes decisions and evidence.

Day 1

We connect images, Pods, workload controllers, probes, and release behavior to the customer's task. You will distinguish serving requests from completing a bounded computation.

Hands-on exercises

  • Identify the runtime and resource requirements of a reference workload.
  • Define an observable execution result beyond Pod startup.

Day 2

You will use Helm and compare Kustomize for repeatable supporting resources. We examine controllers, nodes, and added device capabilities to locate the owner of each requirement.

Hands-on exercises

  • Inspect a workload configuration and its external dependencies.
  • Map a failed execution condition to the responsible platform or specialist component.

Day 3

We examine DNS, Services, ingress and mesh considerations, network policies, and scheduling. The exercise connects device, data, and placement requirements instead of treating them independently.

Hands-on exercises

  • Diagnose a resource or volume constraint that prevents placement.
  • Trace access to a required data endpoint under the selected network controls.

Day 4

You will connect persistent outputs, credentials, metrics, authentication, and RBAC to the workload. We distinguish execution evidence from broader performance and production-readiness claims.

Hands-on exercises

  • Verify the reference workload's permitted access to input and output data.
  • Present the observed result, unverified requirements, and next technical owner.

Your customer workloads, available infrastructure, and acceptance responsibilities can shape the agenda. Get in touch to tailor the workshop to your AI infrastructure discussions.

Separate allocated resources from useful application progress

When an engineer treats device allocation as proof of readiness, the instructor can inspect runtime compatibility and data access. The team can define the next observation and identify where specialist investigation is required.

Asked what she liked most about the course, Veena wrote:

Content was good and enough depth

— Veena, Product Manager at F5.

Bring the workload and the customer acceptance question

Tell us which AI infrastructure requirements your team defines, which Kubernetes decisions it owns, and what customers need to verify. We will recommend exercises within that technical scope.

Discuss your team's training