Kubernetes training for customer migration and cutover engineers

We offer private, instructor-led training for migration engineers who need to validate Kubernetes workloads as part of customer application cutovers.

Your team understands the migration goal. A successful deployment does not establish a successful cutover, especially when data, traffic, access, and rollback responsibilities change together.

LearnKube connects workload behavior to a representative cutover plan, with explicit acceptance checks, stop conditions, and evidence for customer decisions.

Discuss your team's training

Learning in practice

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.

Connect workload validation to the customer's cutover decision

Prepare target validation by identifying workload, access, and dependency requirements, so the customer can judge whether the destination supports the intended use.
Preserve required service behavior by defining traffic and recovery checks, so a deployment change does not conceal an incomplete cutover.
Diagnose cutover blockers by comparing rollout, readiness, and application evidence, so engineers can distinguish a target-platform fault from another migration dependency.
Establish decision ownership by recording acceptance, stop, and recovery responsibilities, so the customer and delivery team know who acts on each result.

A workload rollout and a customer cutover have different scopes

A Kubernetes Deployment rollout reports progress for its managed replicas. A readiness probe determines whether a Pod receives Service traffic. Neither alone establishes that customer data, identity, integrations, and traffic ownership are correct after migration. Engineers need separate application acceptance and recovery criteria around the Kubernetes checks used during the cutover.

I separate a workload rollback from a customer cutover rollback. Once traffic or data ownership changes, restoring a Pod template alone does not necessarily restore the previous service contract or recover changes made elsewhere.
— Daniele Polencic, LearnKube founder and Kubernetes instructor

Customer task or requirement	Kubernetes decision	Practice
Validate target workloads	Rollout and readiness evidence	Inspect an incomplete release
Confirm customer connectivity	Routes and dependency access	Exercise an application path
Decide whether to proceed	Acceptance and stop criteria	Review a cutover checkpoint
Prepare recovery actions	Workload and data boundaries	Rehearse a recovery decision

Your team is not alone in customer cutover delivery

GitLab migration engineers help customers move from self-managed GitLab to GitLab Dedicated. Their work covers assessment, replication, cutover, connectivity, target validation, and repeatable runbooks in existing Kubernetes environments.

For teams supporting that kind of engagement, we recommend a four-day workshop on Kubernetes evidence and recovery boundaries. The exercises connect workload checks to the wider data-migration and cutover plan.

A recommended four-day workshop for migration engineers

This proposed agenda uses a representative cutover scenario. Application-specific data migration remains a separate dependency in the exercise.

Day 1

Releases and target acceptance

We examine images, workload controllers, probes, and rollout behavior. You will separate Kubernetes status from the application checks required by the customer.

Hands-on exercises

Release a sample target workload and inspect its readiness.
Identify an acceptance check that fails despite a completed rollout.

Day 2

Repeatable configuration and recovery boundaries

You will use Helm and compare Kustomize for target configuration. We trace architecture and controller behavior to identify what workload rollback changes and what it leaves outside its scope.

Hands-on exercises

Compare source assumptions with target configuration.
Rehearse a rollout rollback and record the external state it does not restore.

Day 3

Connectivity, policies, and placement

We trace DNS, Services, ingress, network policies, service mesh use cases, and scheduling. The focus is observable connectivity across the intended customer paths.

Hands-on exercises

Verify a representative client and dependency connection.
Diagnose a capacity or routing blocker before the cutover checkpoint.

Day 4

Data, access, metrics, and customer decisions

You will examine persistent state, secrets, autoscaling metrics, authentication, and RBAC. We connect those requirements to a concise acceptance and recovery record.

Hands-on exercises

Inspect a storage or credential dependency that prevents target use.
Explain whether the observed evidence supports proceeding, stopping, or escalating.

Your customer migrations, downtime constraints, and recovery responsibilities can shape the agenda. Get in touch to tailor the workshop to your cutover delivery work.

Challenge an incomplete success signal

When an engineer treats a completed rollout as cutover approval, the instructor can introduce a failed customer interaction. The group can distinguish workload recovery from the wider decision to move traffic or data ownership.

Good intro on Kubernetes and simplifying its complexities. Instruction was at a measured pace and well articulated.
— Kyle, Senior DevOps Consultant at Mission Cloud.

Start with the cutover and its decision boundaries

Tell us which customer migrations your team supports, what the destination must demonstrate, and who owns acceptance and recovery. We will recommend exercises for the Kubernetes part of that work.

Discuss your team's training