DevOps Course in Nepal

AI-INTEGRATED LEARNING: WHAT YOU DO AT EVERY STAGE

This course doesn't just teach you DevOps it teaches you how to work with AI the way modern platform and infrastructure teams already do. Every phase includes real AI tools, applied to real lab tasks, with a concrete deliverable.

What Changes When You Work with AI in DevOps

In 2026, DevOps engineers don't write every YAML file, Dockerfile, or Terraform script from a blank page, and they don't manually stare at dashboards waiting for something to break. AI handles the repetitive, pattern-based parts of the job and engineers spend their time on architecture decisions, incident response judgment, and the infrastructure design choices that actually require human accountability.

Here is how AI changes the real day-to-day in this field, and what we teach you to do with it:

• Writing config files & scripts: What used to take 20–30 minutes of copying boilerplate YAML or Terraform syntax from documentation now takes a few minutes with GitHub Copilot or ChatGPT generating a first draft. You review, correct, and adapt it to your actual environment you don't type it from scratch.
• Debugging pipeline failures: Instead of manually tracing through Jenkins logs line by line, you paste the error into ChatGPT or Gemini to get a fast first diagnosis, then verify it against your actual pipeline. This cuts a 45-minute debugging session down to 10–15 minutes in many cases.
• Monitoring & incident response (AIOps): Traditional monitoring waits for a threshold to be crossed before alerting you. AI-assisted monitoring tools analyze patterns in logs and metrics to flag anomalies before they become outages shifting the engineer's role from "firefighter reacting to alerts" to "reviewer validating AI-flagged risks."
• Infrastructure as Code review: Before applying a Terraform plan to production infrastructure, you can have an AI tool review the plan for common misconfigurations (open security groups, missing tags, overly broad IAM permissions) as a first-pass check — catching mistakes before they become security incidents.

The rule we teach: AI speeds up the repetitive parts of the pipeline. The infrastructure design decisions, security judgment, and production accountability stay with you. Every AI-assisted script or config in class is reviewed and explained by the student before it ships.

DEVOPS COURSE CURRICULUM

Skill Shikshya's DevOps Course in Nepal is structured to take you from zero infrastructure knowledge to a confident, job-ready DevOps engineer. Every module includes practical lab exercises, real cloud infrastructure builds, container deployment projects, and CI/CD pipeline automation challenges so you graduate with both proven skills and a portfolio of real DevOps work.

The curriculum covers the complete DevOps engineering roadmap: Linux system administration, Git and GitHub, Docker containerization, Kubernetes orchestration, CI/CD automation with Jenkins and GitHub Actions, AWS and Azure cloud computing, Terraform and Ansible Infrastructure as Code, Prometheus and Grafana monitoring, ELK Stack logging, web application security basics, and portfolio development the full DevOps skill set that tech companies, software startups, and IT firms in Nepal and globally actively hire for.

Below is the complete Devops Roadmap covered in the course: