DevOps Uncomplicated Hands-On Automation

What you'll learn
Automate infrastructure on AWS and Azure using Terraform (VMs, networks, Kubernetes)
Build professional CI/CD pipelines with GitHub Actions (tests, deploy, auto-destroy)
Manage Kubernetes clusters on Azure (AKS) with HPA, Ingress, and KEDA
Monitor cloud environments with Datadog (metrics, logs, dashboards)
Write Ansible playbooks for automated server configuration
Work with containers (Docker multistage, Docker Compose, Docker Hub)
Set up complete DevOps environments (Git, Linux, IaC, CI/CD)
Prepare for certifications like AZ-400 DevOps
Example: Conduct a case study to manage a project from concept to completion
Requirements
Basic IT Knowledge - Simple understanding of how servers and networks work (beginner level)
Computer with Internet Access - Windows, Mac, or Linux (for hands-on labs)
Free Cloud Accounts - AWS Free Tier and Azure Free Tier (we teach you how to set them up)
Willingness to Learn - The course starts from zero with Git, Linux, and cloud concepts
No programming experience required - You'll learn everything you need step by step
Description
WHAT YOU'LL LEARN - HANDS-ON, NO FLUFFCloud, Hands-OnBuild VMs, networks, and storage on AWS and Azure (Labs 10-15, 38-44)Real AutomationWrite scripts to create multiple VMs in bulk (Labs 40, 44) and manipulate JSON using jq (Labs 41, 43)Professional TerraformProvision Kubernetes clusters (Labs 52-55) and VMs with variables and modules (Labs 48-51)Applied AnsibleDeploy containers, manage users and permissions (Labs 60-61)Complete CI/CDBuild a GitHub Actions pipeline with testing, deployment, and auto-destroy infrastructure (Labs 64-80)Kubernetes on AzureMaster HPA, Ingress, KEDA, and Probes (Labs 91-93)Elite MonitoringCreate Datadog dashboards for Kubernetes and EC2 with visual alerts (Labs 97-102)NEW MODULESAI for DevOps → ChatGPT, Claude, Gemini (in progress)Vibe Coding → For DevOps workflowsPython → For DevOps automationDIRECT, TO-THE-POINT METHODOLOGYEach lesson = 1 practical skillNo disconnected theory → everything has a real-world applicationReusable labs → apply scripts directly at workFrom zero to pro → start with Git/Linux and evolve into full DevOps projectsWHAT YOU'LL BUILDA multi-cloud environment (AWS + Azure) using CLI and Terraform (Sections 2, 6, 7)A CI/CD pipeline that tests, deploys, and auto-destroys infrastructure (Section 9)A Kubernetes cluster with metric-based autoscaling (Lab 91)A Datadog monitoring system with visual alerts (Lab 101)WHY THIS COURSE IS DIFFERENTFocus on troubleshooting (Labs 79, 100) - a key skill most courses skipAligned with the AZ-400 certification - many students are already certifiedReal-world tools → Terraform, Ansible, GitHub Actions, Helm, KEDA, DatadogWHO THIS COURSE IS FORDevOps beginners (we start with Git and Linux - Sections 1, 3, 4)Developers who want to understand and implement CI/CDSysadmins migrating to cloud environmentsFREQUENTLY ASKED QUESTIONSDo I need experience?No. You'll learn step by step, starting from Git and Linux all the way to advanced DevOps projects.Can I apply this at work?Absolutely. The labs use real-world tools and free accounts on AWS and Azure.What makes this course different?No pointless "hello world" exercisesReal Terraform + Kubernetes + Helm labsMulti-cloud automation with AWS and AzureA practical, automation-focused approach for real projectsEXCLUSIVE GUARANTEELifetime access to 103 fully hands-on lessonsUdemy certificate of completionAll scripts included - ready to use in your day-to-day workThis course makes limited use of artificial intelligence tools for language adjustments and improvements in clarity. All course content, labs, and explanations were created and reviewed by the instructor.
Who this course is for
Infrastructure and support professionals looking to transition into DevOps, or anyone curious about DevOps but unsure where to start
Python for DevOps enthusiasts seeking practical applications
Java developers wanting to integrate DevOps practices
Support analysts aiming to advance into automation and cloud environments
Professionals interested in Artificial Intelligence applied to DevOps
Example: Beginner Python developers curious about data science
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