Kubernetes Engineer – Cluster Deployment

• Declarative model: defining the desired state and understanding how Kubernetes continuously maintains it.
• Control plane and worker nodes: component responsibilities and interaction principles.
• Core Kubernetes resources and the problems each resource solves.
• Network isolation and access control within a cluster.
• Working with multiple clusters and switching between contexts.
• Building a local cluster and performing initial health diagnostics.
• Lab: Deploy a three-node local Kubernetes cluster and perform initial diagnostics of its components.

• Helm as the Kubernetes package manager: chart structure, release management, and rollbacks.
• Ingress controllers: how external traffic is routed to the correct application workload.
• TLS certificate lifecycle management: issuance, storage, and automatic renewal.
• Resource consumption metrics as the foundation for automated cluster decisions.
• Horizontal autoscaling: scaling triggers and operational mechanisms.
• Namespaces as the primary unit for environment isolation.
• Lab: Deploy the core infrastructure stack, including ingress, TLS certificates, metrics collection, and autoscaling. 

• Helm chart structure: templates, values, and helper functions.
• Container hardening: least-privilege access, read-only file systems, and restricted system calls.
• Network policies: allowing applications to receive traffic only from approved sources.
• A single deployment template for multiple environments: managing differences between development, testing, and production.
• Environment isolation using namespaces: what is isolated and what remains shared.
• Application upgrades and rollbacks using Helm and native Kubernetes mechanisms.
• Lab: Build a Helm chart from scratch and deploy it across three isolated environments using a single reusable template.

• Metrics collection using the pull model: how monitoring systems gather data from cluster components.
• Metrics visualization and dashboard design.
• Alert management: grouping, routing, and duplicate suppression.
• Centralized log collection: storage and label-based search without full-content indexing.
• Resource utilization analysis and automated configuration recommendations.
• Data flow architecture: how events move from containers to visualization and monitoring interfaces.
• Lab: Deploy a complete observability stack, including metrics, logs, alerts, and resource recommendations in a unified interface. 

• Layer 1 – Image: Selecting secure base images and performing automated vulnerability scanning.
• Layer 2 – Build: Secret detection, static code analysis, dependency inventory management, and image signing.
• Layer 3 – Container: Applying least-privilege principles and restricting system calls.
• Layer 4 – Admission Control: Policies that block non-compliant configurations before deployment.
• Layer 5 – Secrets Management: Secure storage of sensitive data and integration with external secret management systems.
• Layer 6 – Runtime Security: Detecting anomalous container behavior using eBPF-based monitoring.
• Layer 7 – Delivery Pipeline: Mandatory approval workflows and image verification before deployment.
• Lab: Build a seven-layer security model, from image validation during build time to real-time threat detection in production.

• Continuous integration pipeline architecture: build matrices and reusable workflow stages.
• Multi-platform container image builds and publishing to container registries.
• Complete image lifecycle management: from code commit to a signed artifact with a dependency inventory.
• Delivery stage: approval gates, signature verification, re-scanning, and deployment.
• Pipeline troubleshooting: a systematic approach to identifying and resolving failures.
• Local execution and testing of pipeline stages without running the remote CI/CD system.
• Lab: Build a two-stage CI/CD pipeline with security checks, artifact signing, and deployment approval gates. 

• Build cloud infrastructure using declarative configuration: cluster, networking, roles, and supporting services.
• Identity federation: allowing cluster components to access cloud APIs without storing static credentials.
• Using managed load balancing services instead of in-cluster ingress controllers.
• Storing metrics and logs in managed services outside the cluster.
• Agentless threat detection inside pods using native cloud security services.
• Managing secrets through external secret stores synchronized with the cluster.
• Built-in access control and authorization without additional cluster components.
• Authenticating deployment pipelines in AWS through identity federation rather than long-lived credentials.
• Lab: Migrate the complete Kubernetes stack to AWS, replacing local infrastructure components with managed cloud services.

• Build cloud infrastructure: cluster, virtual network, secret storage, and log analytics workspace.
• Azure networking model: pod IP addresses as part of the virtual network address space.
• Managing inbound traffic through Azure Application Gateway.
• Identity federation using Azure Active Directory and managed identities.
• Built-in monitoring and logging without self-managed observability components.
• Runtime container protection and vulnerability analysis using Azure security services.
• Comparing AWS EKS and Azure AKS architectures: similarities, differences, and design trade-offs.
• Lab: Deploy the same Kubernetes stack in Microsoft Azure while adapting it to Azure-specific architectural patterns and services. 

• Build cloud infrastructure: cluster, networking, service accounts, and secret management.
• Google Cloud networking model: direct pod IP routing without an additional network layer.
• Gateway API: separating gateway resources from routing rules, with control managed outside the cluster.
• Managing TLS certificates entirely outside the Kubernetes cluster.
• Identity federation through node metadata services instead of stored credentials.
• Platform-native threat detection at the kernel and network traffic levels.
• Final comparison of four deployment environments: what remains the same, what changes, and why.
• Lab: Deploy the Kubernetes stack in Google Cloud and perform a comprehensive comparison of the architectural approaches used across all four platforms.

Yes. We work with several government-supported education programs that provide funding opportunities for different categories of applicants, including not only unemployed individuals but also employed residents of Estonia.

Submit an application, and we will help you determine which government funding and support programs may be available in your specific situation.

Basic familiarity with the Linux command line and container technologies is recommended.

No prior experience with Kubernetes is required — the course starts with the fundamentals of container orchestration. Experience with Docker will be an advantage.

Most of the hands-on labs can be completed using the free tiers and trial credits available from AWS, Microsoft Azure, and Google Cloud.

The local Kubernetes cluster used at the beginning of the course does not require any cloud resources. Environment requirements and any expected cloud-related costs will be explained before the course starts.

Kubernetes itself remains largely the same across cloud providers — what changes is the surrounding ecosystem: networking architecture, managed services, security models, and access to cloud APIs.

Understanding the differences between AWS EKS, Azure AKS, and Google Kubernetes Engine (GKE) allows you to work confidently with any of these platforms and make informed decisions about which solution best fits a particular project or business requirement.

The schedule is designed to be flexible, with classes held 2–3 times per week. All course materials remain available to you permanently, so you can review them at your own pace.

If you miss a session, our instructors will help you catch up and stay on track with the program.

You will receive a MYEDU Training Center certificate issued in an international format, as well as a certificate from the S.E. School of Systems Analysis confirming your qualification.

These documents demonstrate your acquired skills and knowledge and are valued by employers in the IT industry.