Development & technology · Kubernetes & Microservices

AI training for Kubernetes and Microservices Teams

Kubernetes has become the standard platform for microservices – and with it comes a flood of YAML manifests, Helm charts, Kustomize overlays, operators, service mesh configuration and GitOps repositories. On top of that, failures are distributed across dozens of services. AI coding assistants and agents take a lot of that complexity off your hands: they generate Helm charts with sensible defaults, diagnose CrashLoopBackOff and OOMKilled errors from events and logs, write NetworkPolicies and RBAC rules on the least-privilege principle and help define service boundaries. At the same time, an agent with cluster access needs firm limits. This course teaches you to use AI for manifests, troubleshooting and architecture work without losing control of your clusters.

Who is this course for?

  • Platform engineers and Kubernetes administrators
  • Developers running microservices on Kubernetes
  • Site reliability engineers with cluster responsibility
  • Software architects designing distributed systems
  • DevOps teams introducing or scaling GitOps

Typical challenges we address

  • Unwieldy amounts of YAML, Helm values and overlays across many environments
  • Difficult troubleshooting of distributed issues spanning services, networking and resource limits
  • Security gaps from overly broad RBAC, missing NetworkPolicies and privileged containers
  • Unclear service boundaries and tight coupling in growing microservice landscapes

Practical scenarios from your field

Creating Helm charts and Kustomize overlays

The agent generates Helm charts or Kustomize overlays with probes, resource requests, pod disruption budgets and security contexts, validates them with helm lint and kubeconform and documents every value.

Debugging pods and deployments

From kubectl output, events and container logs, the AI narrows down causes of CrashLoopBackOff, OOMKilled, ImagePullBackOff or failing readiness probes and proposes concrete fixes. The team reviews every proposed fix before applying it.

RBAC and NetworkPolicies

The AI analyses existing roles and traffic flows, writes least-privilege RBAC rules and creates NetworkPolicies that allow only the traffic services genuinely need. Changes are tested in a non-production cluster first.

Service boundaries and communication

The assistant helps define service boundaries along business domains, weigh synchronous against asynchronous communication and implement patterns such as outbox, circuit breaker or saga properly.

Setting up observability

Services get OpenTelemetry instrumentation, Prometheus metrics, ServiceMonitors and alert rules, plus dashboards that make latency, error rate and saturation visible. Alerts are aligned with service level objectives so that only relevant incidents escalate.

Maintaining GitOps repositories

The AI structures repositories for Argo CD or Flux, separates environments cleanly, automates image updates and explains sync failures using status output and diffs. Drift between repository and cluster becomes visible.

Possible hands-on projects

Manifest linter with a rule set

A tool that checks Kubernetes manifests for missing limits, probes, labels and insecure settings and produces a report for pull requests.

Microservice demo with tracing

Two or three small services instrumented with OpenTelemetry, deployed via Helm to a local cluster, including a dashboard for distributed traces.

Cluster hygiene report

A CLI that finds unused ConfigMaps, orphaned PersistentVolumes and deployments without resource settings and suggests clean-up actions. Nothing is deleted without human approval.

All 10 courses for Kubernetes and Microservices Teams

Vibe & agentic coding (10)

Data protection & compliance

Kubeconfigs, service account tokens and secrets must never end up in prompts, and agents should only work with read-only or tightly scoped rights in non-production clusters. Check container images and charts for provenance, signatures and vulnerabilities, and take into account the supply-chain and SBOM requirements of NIS2 and the Cyber Resilience Act.

Prerequisites

Practical experience with containers, kubectl and core Kubernetes objects, plus a working knowledge of Helm, YAML and Git.

Frequently asked questions

Should an AI agent have direct access to my cluster?

Only in a controlled way. For analysis and troubleshooting, read-only rights in selected namespaces are usually enough, while changes should go through GitOps repositories and reviews. On the course you set up dedicated service accounts, local test clusters and approval processes so the agent is useful without creating production risk.

Does the course also cover microservice architecture questions?

Yes. Beyond manifests and operations, we look at how AI can support defining service boundaries, weighing communication patterns and assessing architecture options. The AI provides options and arguments, while the decision stays with the team and is documented transparently. That keeps decisions accountable.

Request without obligation

We will get back to you within one working day with a confirmation or an alternative suggestion.

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Total €949 plus VAT