Decoding DevOps – From Basics to Advanced Projects with AI

62 hours 3 Enrolled No ratings yet All Levels

This course is designed for anyone who wants to start or advance their DevOps career through hands-on, project-based learning.

You’ll begin with Linux, networking, and scripting fundamentals, then progress through key DevOps tools — Git, Jenkins, GitHub Actions, GitLab, Terraform, Ansible, Docker, Kubernetes, and AWS Cloud.

Each step builds on the last with real projects, like setting up the Vprofile application across multiple DevOps stages — from on-premise to AWS to Kubernetes.

The course also introduces AI-powered tools such as GitHub CopilotAmazon Q, and AI-integrated Helm, helping you automate faster, code smarter, and build intelligent DevOps pipelines.

And now — introducing the brand-new Monitoring & Observability section, where you’ll learn how to collect, visualize, and analyze metrics, logs, and traces using tools like Prometheus, Grafana, Loki, and Alloy.

By the end, you’ll master both the core DevOps practices and modern AI-driven workflows, preparing you for real-world cloud and automation environments.

Step-by-Step Learning Path

Step 1

  • Basics of Linux

  • Server Management in Linux

  • Vagrant

  • Basics of Networking

  • Project: VProfile Project Intro & Setup on VMs

Step 2

  • YAML & JSON

  • Bash Scripting (Variables, Conditions, Loops)

  • Automating Admin Tasks

  • GitHub Copilot as AI Assistant for Scripting

Step 3

  • Cloud Computing Intro

  • IAM, EC2, EBS, ELB

  • SSM & CloudShell Intro

  • AWS CLI, S3, CloudWatch, RDS, Auto Scaling, Route53

  • Project: Lift & Shift Web App to AWS

  • Re-Architecting Web App on AWS (PaaS & SaaS)

Step 4

  • Git & GitHub

  • Maven Build Tools

  • Jenkins (CI/CD Pipelines, Master/Slave, Nexus, SonarQube)

  • GitHub Actions (Workflows, Runners, Security Scans)

  • GitLab CI/CD (Pipelines, Stages, Docker Integration)

Step 5

  • Python Scripting

  • Automating OS Tasks

  • Python for AWS using Amazon Q (AI)

Step 6

  • Terraform Fundamentals (Variables, Modules, Backends)

  • Infrastructure as Code for VPC Setup

Step 7 – Monitoring & Observability

  • Introduction to Monitoring & Observability

  • Why Monitoring is Essential for DevOps

  • Setting up Prometheus, Grafana, Loki, and Alloy

  • Writing Queries with PromQL

  • Connecting Grafana Data Sources

  • Building Dashboards and Alerts

  • Slack Integrations for Real-Time Notifications

  • Integrating Loki and Alloy for Logs and Metrics

Step 8

  • Ansible Intro

  • Ad Hoc Commands, Modules, YAML Basics

  • Playbooks, Variables, Conditions, Loops

  • Handlers, Templates, Roles

  • Ansible for AWS Automation

Step 9

  • AWS (VPC Deep Dive, Lambda, Logging, Custom Metrics)

  • Project: CI/CD on AWS – Beanstalk, RDS, CodePipeline

Step 10

  • Docker (Containers, Images, Volumes, Networks)

  • Kubernetes (Setup, Objects, Autoscaling, Ingress, ConfigMaps)

  • Helm with AI, Lens

  • Project: VProfile Deployment on Kubernetes

Step 11

  • Project on GitOps

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What's included

  • 62 hours on-demand video
  • 13 articles
  • 121 downloadable resources
  • Access on mobile and TV
  • Certificate of completion

Requirements

  • Basic Computer Knowledge

Audience

  • Anybody who wants to Learn DevOps

What Will I Learn?

  • Learn DevOps from total scratch
  • Linux and Server Management (Gemini CLI)
  • Networking fundamentals & Vagrant setup
  • YAML, JSON, and Bash scripting with GitHub Copilot (AI)
  • AWS Cloud (IAM, EC2, S3, RDS, EBS, ELB, Systems Manager, Lambda, VPC, Amazon Q, CloudWatch, Auto Scaling, Route 53)
  • Build & Test Automation using Git, Maven, Jenkins, GitHub Actions, and GitLab CI/CD
  • CI/CD Pipelines and DevOps Projects with Nexus, SonarQube & Slack integration
  • Python scripting Basics and for Automation and AWS tasks with Amazon Q (AI code assistant)
  • Infrastructure as Code using Terraform (VPC, modules, backends)
  • Configuration Management using Ansible
  • Monitoring & Observability with Prometheus, Grafana, Loki, Alert manager & Alloy
  • Docker and Kubernetes (production-grade setup, Helm with AI, Lens)
  • AWS DevOps Services: CodeCommit, CodeBuild, CodePipeline, Beanstalk, Lambda
  • GitOps Project — end-to-end CI/CD workflow with Git as the single source of truth

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