Hexadigitall Technologies logo
Hexadigitall Technologies https://hexadigitall.com
QR code to the DevOps Kubernetes Mastery page
Scan to open the course page and view enrollment options.

Course Snapshot

Automate everything. Master CI/CD, Docker containerization, and Kubernetes orchestration to ship reliable software faster.

⏱️16 Weeks
📊Advanced
🚀Ship Faster
Course QR Code

DevOps Engineering & Kubernetes Mastery

Master production delivery systems with CI/CD, Docker, Kubernetes, observability, GitOps, platform engineering, and secure deployment workflows.

Duration: 16 Weeks
Level: Advanced
Study Time: 5 hours/week + labs
School: Cloud & DevOps

Welcome to DevOps Engineering & Kubernetes Mastery! 🎓

This curriculum for DevOps Engineering & Kubernetes Mastery follows a Bloom-aligned progression from advanced analysis to architecture-grade creation, with weekly evidence, labs, and portfolio outputs matched to advanced expectations.

Each week advances from comprehension and application toward evaluation and creation, ensuring progressive learning and capstone readiness.

Your success is our priority. By the end of the course, you will be able to design and defend a production-ready Kubernetes-based delivery platform with security, automation, and operational maturity built in. You will graduate with a professionally curated portfolio that demonstrates scope, depth, and delivery quality. You will graduate with a professionally curated portfolio that demonstrates scope, depth, and delivery quality. You will graduate with a professionally curated portfolio that demonstrates scope, depth, and delivery quality. You will graduate with a professionally curated portfolio that demonstrates scope, depth, and delivery quality.

Prerequisites & What You Should Know

  • Experience designing system architectures with distributed components and resilience patterns
  • Hands-on practice with Infrastructure-as-Code, cloud service models (IaaS/PaaS/SaaS), and networking basics
  • Understanding of auto-scaling, load balancing, and cloud cost optimization tradeoffs
  • Familiarity with at least one major cloud console (AWS, Azure, or GCP) and basic CLI usage

Recommended Complementary Courses

Cloud Networking

Master virtual networks, security groups, and multi-region routing

DevOps & Infrastructure Automation

Deepen CI/CD pipeline integration and infrastructure drift detection

Cloud Cost Optimization

Learn resource tagging, rightsizing, and multi-cloud cost analysis

Essential Learning Resources

  • Cloud architecture reference implementations and Well-Architected Framework reviews
  • Infrastructure-as-Code templates and cloud service comparison matrices
  • Cost optimization playbooks and resilience validation runbooks

Your Learning Roadmap

  • Early Weeks: Core cloud services, networking fundamentals, and basic deployments
  • Middle Weeks: Advanced architectures, multi-region deployment, and disaster recovery
  • Late Weeks: Cost optimization, governance frameworks, and production readiness

Detailed Weekly Curriculum

Week 15 hours + labs
DevOps Foundations
  • Analyze the principles of DevOps Foundations and link them to course outcomes at advanced depth with architecture-level decision quality.
  • Evaluate DevOps Foundations in a guided scenario using realistic tools, constraints, and quality gates.
  • Design trade-offs, risks, and decision points for DevOps Foundations, then record rationale for stakeholder review.
  • Justify a portfolio-ready architecture decision record for DevOps Foundations with measurable success criteria and next actions.

Lab Exercise

  • Implement a production-style DevOps Foundations setup using versioned config/code and environment controls.
  • Run validation gates for DevOps Foundations covering reliability, security, and rollback readiness.
  • Publish DevOps Foundations execution evidence with logs, metrics, and troubleshooting notes.
Week 25 hours + labs
CI/CD Pipelines
  • Analyze the principles of CI/CD Pipelines and link them to course outcomes at advanced depth with architecture-level decision quality.
  • Evaluate CI/CD Pipelines in a guided scenario using realistic tools, constraints, and quality gates.
  • Design trade-offs, risks, and decision points for CI/CD Pipelines, then record rationale for stakeholder review.
  • Justify a portfolio-ready architecture decision record for CI/CD Pipelines with measurable success criteria and next actions.

Lab Exercise

  • Implement a production-style CI/CD Pipelines setup using versioned config/code and environment controls.
  • Run validation gates for CI/CD Pipelines covering reliability, security, and rollback readiness.
  • Publish CI/CD Pipelines execution evidence with logs, metrics, and troubleshooting notes.
Week 35 hours + labs
Docker & Images
  • Analyze the principles of Docker & Images and link them to course outcomes at advanced depth with architecture-level decision quality.
  • Evaluate Docker & Images in a guided scenario using realistic tools, constraints, and quality gates.
  • Design trade-offs, risks, and decision points for Docker & Images, then record rationale for stakeholder review.
  • Justify a portfolio-ready architecture decision record for Docker & Images with measurable success criteria and next actions.

Lab Exercise

  • Implement a production-style Docker & Images setup using versioned config/code and environment controls.
  • Run validation gates for Docker & Images covering reliability, security, and rollback readiness.
  • Publish Docker & Images execution evidence with logs, metrics, and troubleshooting notes.
Week 45 hours + labs
Orchestrating Containers
  • Analyze the principles of Orchestrating Containers and link them to course outcomes at advanced depth with architecture-level decision quality.
  • Evaluate Orchestrating Containers in a guided scenario using realistic tools, constraints, and quality gates.
  • Design trade-offs, risks, and decision points for Orchestrating Containers, then record rationale for stakeholder review.
  • Justify a portfolio-ready architecture decision record for Orchestrating Containers with measurable success criteria and next actions.

Lab Exercise

  • Design and execute a concrete Orchestrating Containers build in DevOps Engineering & Kubernetes Mastery with a clear acceptance checklist.
  • Validate Orchestrating Containers with objective tests and quality controls before review.
  • Deliver Orchestrating Containers artifacts with reproducible steps and operational notes.
Week 55 hours + labs
State & Storage
  • Analyze the principles of State & Storage and link them to course outcomes at advanced depth with architecture-level decision quality.
  • Evaluate State & Storage in a guided scenario using realistic tools, constraints, and quality gates.
  • Design trade-offs, risks, and decision points for State & Storage, then record rationale for stakeholder review.
  • Justify a portfolio-ready architecture decision record for State & Storage with measurable success criteria and next actions.

Lab Exercise

  • Design and execute a concrete State & Storage build in DevOps Engineering & Kubernetes Mastery with a clear acceptance checklist.
  • Validate State & Storage with objective tests and quality controls before review.
  • Deliver State & Storage artifacts with reproducible steps and operational notes.
Week 65 hours + labs
Security & Policy
  • Analyze the principles of Security & Policy and link them to course outcomes at advanced depth with architecture-level decision quality.
  • Evaluate Security & Policy in a guided scenario using realistic tools, constraints, and quality gates.
  • Design trade-offs, risks, and decision points for Security & Policy, then record rationale for stakeholder review.
  • Justify a portfolio-ready architecture decision record for Security & Policy with measurable success criteria and next actions.

Lab Exercise

  • Implement baseline controls for Security & Policy and verify enforcement on target systems.
  • Run assessment/scanning for Security & Policy and prioritize findings by exploitability and impact.
  • Close critical findings for Security & Policy and publish re-test evidence.
Week 75 hours + labs
Service Mesh & Traffic
  • Evaluate the principles of Service Mesh & Traffic and link them to course outcomes at advanced depth with architecture-level decision quality.
  • Design Service Mesh & Traffic in a guided scenario using realistic tools, constraints, and quality gates.
  • Optimize trade-offs, risks, and decision points for Service Mesh & Traffic, then record rationale for stakeholder review.
  • Justify a portfolio-ready architecture decision record for Service Mesh & Traffic with measurable success criteria and next actions.

Lab Exercise

  • Design and execute a concrete Service Mesh & Traffic build in DevOps Engineering & Kubernetes Mastery with a clear acceptance checklist.
  • Validate Service Mesh & Traffic with objective tests and quality controls before review.
  • Deliver Service Mesh & Traffic artifacts with reproducible steps and operational notes.
Week 85 hours + labs
Observability
  • Evaluate the principles of Observability and link them to course outcomes at advanced depth with architecture-level decision quality.
  • Design Observability in a guided scenario using realistic tools, constraints, and quality gates.
  • Optimize trade-offs, risks, and decision points for Observability, then record rationale for stakeholder review.
  • Justify a portfolio-ready architecture decision record for Observability with measurable success criteria and next actions.

Lab Exercise

  • Build a practical Observability workflow with source-to-output transformation steps.
  • Validate Observability quality and performance with objective checks and tuning updates.
  • Deliver reproducible Observability outputs (dashboard/report/notebook) with run instructions.
Week 95 hours + labs
Helm & Packaging
  • Evaluate the principles of Helm & Packaging and link them to course outcomes at advanced depth with architecture-level decision quality.
  • Design Helm & Packaging in a guided scenario using realistic tools, constraints, and quality gates.
  • Optimize trade-offs, risks, and decision points for Helm & Packaging, then record rationale for stakeholder review.
  • Justify a portfolio-ready architecture decision record for Helm & Packaging with measurable success criteria and next actions.

Lab Exercise

  • Design and execute a concrete Helm & Packaging build in DevOps Engineering & Kubernetes Mastery with a clear acceptance checklist.
  • Validate Helm & Packaging with objective tests and quality controls before review.
  • Deliver Helm & Packaging artifacts with reproducible steps and operational notes.
Week 105 hours + labs
Scaling & Performance
  • Evaluate the principles of Scaling & Performance and link them to course outcomes at advanced depth with architecture-level decision quality.
  • Design Scaling & Performance in a guided scenario using realistic tools, constraints, and quality gates.
  • Optimize trade-offs, risks, and decision points for Scaling & Performance, then record rationale for stakeholder review.
  • Justify a portfolio-ready architecture decision record for Scaling & Performance with measurable success criteria and next actions.

Lab Exercise

  • Design and execute a concrete Scaling & Performance build in DevOps Engineering & Kubernetes Mastery with a clear acceptance checklist.
  • Validate Scaling & Performance with objective tests and quality controls before review.
  • Deliver Scaling & Performance artifacts with reproducible steps and operational notes.
Week 115 hours + labs
GitOps
  • Evaluate the principles of GitOps and link them to course outcomes at advanced depth with architecture-level decision quality.
  • Design GitOps in a guided scenario using realistic tools, constraints, and quality gates.
  • Optimize trade-offs, risks, and decision points for GitOps, then record rationale for stakeholder review.
  • Justify a portfolio-ready architecture decision record for GitOps with measurable success criteria and next actions.

Lab Exercise

  • Implement a production-style GitOps setup using versioned config/code and environment controls.
  • Run validation gates for GitOps covering reliability, security, and rollback readiness.
  • Publish GitOps execution evidence with logs, metrics, and troubleshooting notes.
Week 125 hours + labs
Resilience & Chaos
  • Design the principles of Resilience & Chaos and link them to course outcomes at advanced depth with architecture-level decision quality.
  • Optimize Resilience & Chaos in a guided scenario using realistic tools, constraints, and quality gates.
  • Architect trade-offs, risks, and decision points for Resilience & Chaos, then record rationale for stakeholder review.
  • Defend a portfolio-ready architecture decision record for Resilience & Chaos with measurable success criteria and next actions.

Lab Exercise

  • Design and execute a concrete Resilience & Chaos build in DevOps Engineering & Kubernetes Mastery with a clear acceptance checklist.
  • Validate Resilience & Chaos with objective tests and quality controls before review.
  • Deliver Resilience & Chaos artifacts with reproducible steps and operational notes.
Week 135 hours + labs
Security Automation
  • Design the principles of Security Automation and link them to course outcomes at advanced depth with architecture-level decision quality.
  • Optimize Security Automation in a guided scenario using realistic tools, constraints, and quality gates.
  • Architect trade-offs, risks, and decision points for Security Automation, then record rationale for stakeholder review.
  • Defend a portfolio-ready architecture decision record for Security Automation with measurable success criteria and next actions.

Lab Exercise

  • Implement baseline controls for Security Automation and verify enforcement on target systems.
  • Run assessment/scanning for Security Automation and prioritize findings by exploitability and impact.
  • Close critical findings for Security Automation and publish re-test evidence.
Week 145 hours + labs
Platform Engineering
  • Design the principles of Platform Engineering and link them to course outcomes at advanced depth with architecture-level decision quality.
  • Optimize Platform Engineering in a guided scenario using realistic tools, constraints, and quality gates.
  • Architect trade-offs, risks, and decision points for Platform Engineering, then record rationale for stakeholder review.
  • Defend a portfolio-ready architecture decision record for Platform Engineering with measurable success criteria and next actions.

Lab Exercise

  • Design and execute a concrete Platform Engineering build in DevOps Engineering & Kubernetes Mastery with a clear acceptance checklist.
  • Validate Platform Engineering with objective tests and quality controls before review.
  • Deliver Platform Engineering artifacts with reproducible steps and operational notes.
Week 155 hours + labs
FinOps & Governance
  • Design the principles of FinOps & Governance and link them to course outcomes at advanced depth with architecture-level decision quality.
  • Optimize FinOps & Governance in a guided scenario using realistic tools, constraints, and quality gates.
  • Architect trade-offs, risks, and decision points for FinOps & Governance, then record rationale for stakeholder review.
  • Defend a portfolio-ready architecture decision record for FinOps & Governance with measurable success criteria and next actions.

Lab Exercise

  • Design and execute a concrete FinOps & Governance build in DevOps Engineering & Kubernetes Mastery with a clear acceptance checklist.
  • Validate FinOps & Governance with objective tests and quality controls before review.
  • Deliver FinOps & Governance artifacts with reproducible steps and operational notes.
Week 165 hours + capstone
Capstone: Prod-Ready Platform
  • Design the principles of Capstone: Prod-Ready Platform and link them to course outcomes at advanced depth with architecture-level decision quality.
  • Optimize Capstone: Prod-Ready Platform in a guided scenario using realistic tools, constraints, and quality gates.
  • Architect trade-offs, risks, and decision points for Capstone: Prod-Ready Platform, then record rationale for stakeholder review.
  • Defend a portfolio-ready architecture decision record for Capstone: Prod-Ready Platform with measurable success criteria and next actions.

Lab Exercise

  • Design and execute a concrete Capstone: Prod-Ready Platform build in DevOps Engineering & Kubernetes Mastery with a clear acceptance checklist.
  • Validate Capstone: Prod-Ready Platform with objective tests and quality controls before review.
  • Deliver Capstone: Prod-Ready Platform artifacts with reproducible steps and operational notes.

Capstone Projects

Project 1: DevOps Engineering & Kubernetes Mastery Foundation Build

Deliver a concrete foundation implementation covering the first phase of the curriculum.

  • Implement and validate DevOps Foundations.
  • Integrate CI/CD Pipelines with reusable workflow standards.
  • Publish evidence for Docker & Images with test and quality artifacts.

Project 2: DevOps Engineering & Kubernetes Mastery Integrated Systems Build

Combine mid-program competencies into a production-style integrated workflow.

  • Build an end-to-end flow around Security & Policy and Service Mesh & Traffic.
  • Add controls, observability, and rollback paths for reliability.
  • Document architecture decisions and trade-offs tied to Observability.

Project 3: DevOps Engineering & Kubernetes Mastery Capstone Delivery

Ship a portfolio-ready capstone with measurable outcomes and stakeholder-ready presentation.

  • Deliver a complete implementation centered on Security Automation.
  • Validate readiness for Platform Engineering using objective acceptance checks.
  • Present final defense and roadmap based on FinOps & Governance outcomes.

Study Tips for Success

  • Allocate two weekly blocks for architecture review, deployment testing, and cost analysis against budgets.
  • Maintain an architecture decision record documenting service choices, redundancy approaches, and cost drivers.
  • Run weekly cost reviews, comparing projected vs. actual spend and identifying optimization leverage points.

About This Course

DevOps Engineering & Kubernetes Mastery is a platform-oriented engineering curriculum focused on the systems that move software safely into production. It is built for advanced learners who want to own automation, cluster operations, delivery controls, and service reliability in one integrated skill set.

  • Best fit roles: DevOps Engineer, Platform Engineer, Site Reliability Engineer, Kubernetes Engineer.
  • Primary outcome: Deliver a secure, observable, and scalable Kubernetes-based DevOps platform.
  • Portfolio value: CI/CD workflows, cluster manifests, GitOps repos, observability dashboards, and resilience playbooks.