Kubernetes training before your first production EKS platform

We offer private, instructor-led training for application and platform teams that plan their first production Kubernetes environment on Amazon EKS.

Your team understands its applications, but it has no existing cluster operating model to adapt. Design choices need practical evidence, including workload requirements, AWS integrations, and the responsibilities the team will retain.

LearnKube uses a representative application to make those decisions observable. Your engineers will experiment with deployment, traffic, state, and access before relying on a production design.

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.

Use the first workload to ask better design questions

Deploy a representative application by defining images, resources, configuration, and health checks, so the team can connect application requirements to Kubernetes behavior.
Evaluate platform assumptions by examining traffic, identity, storage, and recovery, so design choices reflect the workload rather than only a cluster-creation procedure.
Diagnose early failures by inspecting events, logs, endpoints, and permissions, so experiments identify the component or assumption that needs attention.
Define the future operating model by documenting workload, compute, integration, and support responsibilities, so the production plan includes the work the team must own.

A cluster endpoint is only the start of the design

Creating an EKS cluster establishes a Kubernetes environment, but application requirements still drive important choices. Compute options affect capacity and operating responsibilities, while VPC and subnet requirements shape connectivity and available addresses. A representative workload helps the team examine those decisions alongside storage, credentials, and recovery before it commits to the production build.

I use the first workload to expose design assumptions, not to prove that the platform is production-ready. A useful experiment shows what happens to traffic, credentials, and data when the workload or capacity changes.
— Daniele Polencic, LearnKube founder and Kubernetes instructor

Current task or requirement	New concept or decision	Practice
Plan application execution	Workload resources and compute	Deploy a representative service
Design the traffic path	VPC, Services, and ingress	Trace an external request
Plan data requirements	Storage and recovery dependencies	Replace a stateful workload
Assign production duties	Provider and team boundaries	Map an incident response

Case Study: FL97 / Lila Sciences

FL97's team had no Kubernetes cluster yet. It wanted to experiment, identify design problems early, and then build a production EKS platform.

Before the course, Tim explained the goal:

Part of our goal with this effort is to see around some corners based on our needs and setup the new cluster correctly.
— Tim, Senior Director Data, FL97.

LearnKube delivered a private Kubernetes course for the team. The four-day agenda that follows is our recommendation for a team preparing its first EKS environment.

A recommended four-day workshop before the production build

This proposed agenda develops Kubernetes foundations through experiments with a sample application. It makes the relationship between workload choices and AWS infrastructure visible.

Day 1

Images, workloads, and release health

We connect container packaging to Pods, Deployments, and Services. You will define startup and readiness behavior, then examine rolling updates and rollback through an application release.

Hands-on exercises

Deploy a sample service with explicit configuration and health checks.
Introduce an unready version and observe the effect on rollout progress and traffic.

Day 2

Repeatable resources and platform architecture

You will use Helm and compare Kustomize for environment configuration. We explain control-plane, controller, and node responsibilities, then relate them to the selected EKS compute model.

Hands-on exercises

Package the application's resources without hiding its infrastructure dependencies.
Observe workload replacement during a controlled failure and identify what the platform must supply.

Day 3

Network design and capacity placement

We connect DNS, Services, ingress, and network policies to the intended AWS traffic path. You will discuss service mesh use cases, address availability, resource requests, and placement requirements.

Hands-on exercises

Trace the application's route from its external endpoint to a ready Pod.
Diagnose a replica that cannot start because capacity or network requirements are unsatisfied.

Day 4

State, identity, scaling, and ownership

You will examine secrets, persistent storage, and workload access to AWS services. We distinguish HPA behavior from compute capacity and review authentication, RBAC, and the duties required to support the application.

Hands-on exercises

Verify that a replacement workload can access its data and required external service.
Apply load and inspect which resource or operating assumption limits the response.

Your planned workloads, AWS integrations, and future support responsibilities can shape the agenda. Get in touch to tailor the workshop to your first EKS platform.

Turn a design assumption into an observable question

When an engineer assumes a platform option will meet an application requirement, the instructor can define an experiment that exposes the dependency. Your team can gather evidence about traffic, capacity, or credentials before it treats the design as settled.

The course was good. Instructors knowledge was great and ability to cover off important questions was massively important and appreciated. Very interactive and easy to digest, especially on such a broad topic such as kubernetes.
— Hamzah, Quality Engineer at Matillion.

Start with the applications and decisions ahead

Tell us what you plan to run on EKS, which design questions remain open, and who will build and support the platform. We will recommend a course focus and experiments for those decisions.

Discuss your team's training