Advanced Kubernetes Bootcamp : SFD-CKPro – Certified Kubernetes Professional

Categories: DevOps, Kubernetes
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About Course

You already know how to deploy applications on Kubernetes.
Now it’s time to build production-grade systems.

This course is designed for developers and DevOps engineers who have completed the basics of Kubernetes and want to move beyond “it works” to scalable, traffic-aware, secure, and extensible Kubernetes workloads.

In this course, you’ll learn how Kubernetes behaves under real production conditions—how pods are scheduled across nodes and zones, how resources are allocated and scaled, how traffic enters and flows through the cluster, and how Kubernetes can be extended using CRDs and Operators.

You’ll also explore modern Kubernetes traffic management using the Gateway API, understand when (and when not) to introduce a service mesh, and learn how application teams can apply security controls without slowing down development.

Finally, the course introduces you to Agentic Kubernetes—how AI-assisted tools can help debug, operate, and extend Kubernetes systems—setting the stage for more advanced automation and AgenticOps courses later.

This is the natural next step after Kubernetes Essentials and the bridge toward advanced platform engineering, operators, and automation.

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What Will You Learn?

  • Understand and apply advanced pod scheduling using affinity, anti-affinity, and topology spread constraints
  • Manage CPU and memory resources effectively using requests, limits, and Kubernetes autoscaling patterns
  • Implement autoscaling strategies using HPA, VPA, and KEDA based on real workload needs
  • Design modern traffic entry and routing using the Kubernetes Gateway API
  • Apply rate limiting, authentication, and routing strategies for multi-team Kubernetes environments
  • Decide when a service mesh is required—and when it is not
  • Secure Kubernetes workloads using NetworkPolicies, Pod Security Admission, and RBAC (from an application team perspective)
  • Package and manage applications using Helm charts with real-world best practices
  • Extend Kubernetes using Custom Resource Definitions (CRDs)
  • Understand and design Kubernetes Operators, including when not to write one
  • Explore AI-assisted and agentic approaches to Kubernetes debugging and operations (introductory level)

Course Content

Module 0: Introduction and Getting Started
Think of Module 0 as setting the baseline. You'll create a local Kubernetes cluster using KIND, deploy all five components of the voting application, and verify it works end-to-end. This deployment works, but it's not production-ready. Over the next nine modules, you'll evolve this basic setup into a robust, scalable, secure production deployment. By the end of this module, you'll have a working multi-node Kubernetes cluster with the Example Voting App running locally. This exact setup becomes your playground for exploring advanced scheduling, autoscaling, traffic management, security policies, and more.

  • Welcome and About this Course
    02:52
  • Introduction and Getting Started
  • What will you learn ?
    04:58
  • Setting up 3 Node Kubernetes Cluster with KiND
    10:10
  • Deploy the Sample App
    08:10
  • Lab: Setting Up Your Kubernetes Playground
  • Module Wrap Up
    00:48
  • Quiz: Module 0 – Introduction and Getting Started

Module 1: Advanced Pod Scheduling
This module teaches you to take control of the scheduler. You'll learn how to place postgres on SSD nodes, spread vote replicas across different machines, and use taints to keep general workloads off your database servers. These are the first production readiness improvements to your application.

Module 2: Autoscaling – HPA, VPA and KEDA
Autoscaling makes the app self-adjusting. Kubernetes watches metrics, compares them to targets you define, and automatically adjusts replicas or resource allocations. Your Voting App becomes resilient to traffic spikes without manual intervention.

Module 3: Gateway API
In this module, you'll install a Gateway controller (Contour), create Gateway and HTTPRoute resources, implement sophisticated routing rules for the Voting App, and explore traffic splitting patterns for canary deployments. By the end, you'll understand why Gateway API is the future of Kubernetes traffic management.

Module 4: Service Mesh
Service mesh is a crossroads decision in production readiness. Many teams adopt it prematurely, adding complexity without benefit. Others add it too late, missing observability and security gaps. This module teaches you when a service mesh adds value versus when it adds unnecessary overhead.

Module 5: Security – Network Policies, Pod Security Admission , RBAC Policies
This module teaches defense-in-depth security for Kubernetes applications. You'll build multiple layers of protection, each addressing different attack vectors. NetworkPolicy controls traffic at the network level. Pod Security Admission prevents privilege escalation at the pod level. RBAC limits API access. Secrets management protects credentials. Together, these layers create a security posture that's resilient even when individual components are compromised.

Module 6: Writing Helm Charts
Helm is the package manager for Kubernetes. It transforms your collection of YAML files into reusable, parameterized, versionable deployment packages called charts.

Module 7: CRDs
CRDs are the foundation of the Kubernetes extension model. Once you understand CRDs, you unlock Operators (Module 8), Helm patterns, and the entire CNCF ecosystem

Module 8: Building K8s Operators (Workflow)
This module transforms you from "I created a CRD" to "I built an operator that automates reconciliation." You'll build a working VoteConfig operator using Kubebuilder that watches VoteConfig resources and automatically creates and updates ConfigMaps. By the end, you'll understand the complete operator development workflow and how operators make custom resources truly declarative.

Module 9: Intro to Agentic Kubernetes
This module introduces the emerging field of AI-assisted Kubernetes operations. You will learn how Model Context Protocol enables AI models to interact with your cluster safely, try AI-powered troubleshooting on deliberately broken deployments, and develop the safety awareness needed to use these tools responsibly.

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