AI-Assisted DevOps & Cloud Mastery Program (Production-Focused).

About Course

The 𝗔𝗜-𝗔𝘀𝘀𝗶𝘀𝘁𝗲𝗱 𝗗𝗲𝘃𝗢𝗽𝘀 & 𝗖𝗹𝗼𝘂𝗱 𝗠𝗮𝘀𝘁𝗲𝗿𝘆 𝗣𝗿𝗼𝗴𝗿𝗮𝗺 is a job-ready, production-focused training program designed for beginners, professionals, and career switchers who want to build practical skills in DevOps and Cloud Engineering.

You will learn AWS, Linux, Git, Terraform, Ansible, Docker, Kubernetes, CI/CD, ArgoCD, Prometheus, and Grafana through hands-on labs, real-world projects, and an end-to-end capstone project.

This program also teaches you how to use AI assistants safely for scripting, troubleshooting, documentation, command validation, and DevOps workflow improvement.

𝗪𝗵𝗮𝘁 𝗠𝗮𝗸𝗲𝘀 𝗧𝗵𝗶𝘀 𝗣𝗿𝗼𝗴𝗿𝗮𝗺 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 🚀
  • 120+ hands-on labs
  • 5+ real-world projects
  • End-to-end DevOps capstone project
  • Live instructor-led sessions
  • Production-focused labs
  • AI-assisted DevOps workflows
  • GitHub-ready project documentation
  • Resume, LinkedIn, GitHub portfolio, and mock interview support

By the end of this program, you will be ready to build, automate, deploy, monitor, and manage real-world DevOps systems.

Build end-to-end CI/CD pipelines
Deploy applications on AWS Cloud
Containerize applications using Docker
Orchestrate workloads using Kubernetes
Automate infrastructure using Terraform
Configure systems using Ansible
Implement monitoring using Prometheus & Grafana
Manage GitOps deployments using ArgoCD
Use AI assistants for DevOps scripting and troubleshooting
Build real-world DevOps portfolio projects

Course Content

Module 0 — Course Orientation, Tools Setup and Lab Environment

Program Introduction
Complete Course Roadmap
System Requirements and Lab Readiness
Lab Workflow, Learning Material Access, and Assignment Submission
Class Schedule, Guidelines, and Next Steps
Tools and Accounts Setup
AI Assistant Setup for DevOps Learning

Module 1 — DevOps Foundations and SDLC

Introduction to Software Development & Engineering Practices
SDLC Models (Waterfall vs Agile vs DevOps)
Why DevOps in Modern Engineering
DevOps Lifecycle & Key Stages
CI/CD Concepts & Workflow Overview
Monolith vs Microservices Architecture
DevOps Tools Ecosystem Overview
AI Assistant Usage for Understanding DevOps Concepts and Workflows
Self Assessment

Module 2 — AI-Assisted DevOps Overview

Introduction to AI in the DevOps Era
What Are LLMs and How They Work
Types of AI Assistants for DevOps
How AI Assistants Support DevOps Workflows
Use of Generative AI for DevOps Tasks
Overview: AI for Troubleshooting, CI/CD, Monitoring and Logs
Prompting Basics for DevOps
How to Verify AI-Generated Commands and Scripts
AI Hallucination Risks
Do Not Paste Secrets into AI Tools
Human Validation: AI as Assistant, Not Replacement

Module 3 — Linux Administration and Shell Scripting

Linux Architecture & Distributions Overview
File System Structure & Permissions Management
User & Group Management
Package Management (APT, YUM, DNF)
Process, Service & Systemctl Management
Networking Basics & SSH Configuration
Shell Scripting Fundamentals (Bash)
Variables, Loops & Conditional Statements in Scripts
Automating Tasks using Shell Scripts (Cron Jobs)
Linux Security Best Practices
AI Assistant Usage for Linux Commands, Shell Script Generation and Error Debugging
Self Assessment

Module 4 — Networking for Cloud and DevOps

Module 5 — Git and GitHub Version Control

Version Control Fundamentals & Git Concepts
Git Architecture & Workflow
Git Installation & Essential Commands (Clone, Commit, Push, Pull)
Branching Strategies (Feature, Release, Hotfix)
Merging & Handling Conflicts
GitHub Repositories & Collaboration Workflows
Pull Requests, Code Reviews & Team Collaboration
GitHub Actions Basics for CI Integration
Git Best Practices & Repository Management
AI Assistant Usage for Git Commands, Merge Conflicts and Pull Request Review
Self Assessment

Module 6 — AWS Cloud Infrastructure Core

Cloud Computing Fundamentals
AWS Global Infrastructure
IAM Users, Roles & Policies
EC2 Setup & Management
S3 Storage & Lifecycle Policies
VPC, Subnets & Routing
RDS Setup & Multi-AZ Design
DynamoDB & Use Cases
Amazon ECR for Container Images
Amazon ECS Overview
Amazon EKS Cluster Setup Overview
AWS CloudFormation Overview
AI Assistant Usage for AWS CLI Commands, Cloud Errors and Security Review
Self Assessment

Module 7 — Infrastructure as Code with Terraform

Introduction to Infrastructure as Code (IaC)
Terraform Architecture & Workflow
Providers, Resources & Data Sources
State Management & Remote Backends
Modules & Reusable Infrastructure Design
Provision AWS Infrastructure using Terraform
Variables, Outputs & Environment Management
Terraform Best Practices & Project Structure
Terraform Security & State Locking
AI Assistant Usage for Terraform Code Generation, Plan Review and Error Debugging
Self Assessment

Module 8 — Configuration Management with Ansible

Introduction to Configuration Management & Ansible
Ansible Architecture & Setup
Inventory Management & Ad-hoc Commands
Playbooks, Modules & Task Execution
Variables, Facts & Jinja2 Templating
Roles, Collections & Ansible Galaxy
Secrets Management with Ansible Vault
Real-world Automation Use Cases
Ansible Best Practices & Project Structure
AI Assistant Usage for Ansible Playbook Generation, Output Review and Error Debugging
Self Assessment

Module 9 — Containerization with Docker

Virtualization vs Containers
Docker Architecture & Components
Docker Installation & CLI Basics
Images, Containers & Lifecycle Management
Dockerfile & Image Build Optimization
Docker Networking (Bridge, Host, None)
Volumes, Bind Mounts & Storage Management
Docker Compose for Multi-Container Applications
Docker Registry & Image Management (Docker Hub / ECR)
Docker Best Practices & Security
AI Assistant Usage for Dockerfile Generation, Container Errors and Image Optimization
Self Assessment

Module 10 — Kubernetes Container Orchestration

Introduction to Kubernetes & Container Orchestration
Kubernetes Architecture: Cluster, Control Plane, Worker Node & Kubectl
Kubernetes Cluster Setup Overview: Minikube and Amazon EKS
Namespaces, Pods, ReplicaSets, Deployments & Services
StatefulSets, DaemonSets & Jobs
ConfigMaps, Secrets & Environment Variables
Kubernetes Networking: Services, Ingress & Service Discovery
Application Scaling, Auto-Healing & HPA Overview
Rolling Updates, Rollbacks & Deployment Strategies
Kubernetes Storage: Volumes, PV & PVC Overview
Kubernetes Security Basics: RBAC, Service Accounts & Secrets
Monitoring & Logging Overview: kubectl Logs, Events, Prometheus & Grafana
AI Assistant Usage for Kubernetes YAML Review, Pod Errors and kubectl Output Analysis
Self Assessment

Module 11 — CI/CD with Jenkins and GitHub Actions

CI/CD Fundamentals & Pipeline Architecture
Jenkins Installation & Architecture Overview
Jenkins Jobs, Pipelines & Declarative Syntax
GitHub Actions Workflows & Runners
Build Automation & Pipeline Best Practices
Artifact Management
Notifications, Webhooks & Integrations
CI/CD Security & Secrets Management
AI Assistant Usage for Jenkinsfile, GitHub Actions Workflow Generation and Pipeline Debugging
Self Assessment

Module 12 — DevSecOps and Secure CI/CD Pipelines

Introduction to DevSecOps and shift-left security
Secure CI/CD pipeline stages and best practices
Static code analysis with SonarQube
Dependency vulnerability scanning with Snyk
Container image scanning with Aqua Trivy
Secret scanning and secure Git practices
Dockerfile and image security basics
Quality gates and security gates in pipelines
Jenkins and GitHub Actions security integration
Mini project: Secure CI/CD pipeline implementation
AI Assistant Usage for Security Scan Reports, Vulnerability Fixes and Secure Pipeline Review
Self Assessment

Module 13 — GitOps with Argo CD

Introduction to GitOps
Why Argo CD is Used in DevOps
Argo CD Architecture Overview
Installing Argo CD on Kubernetes
Connecting GitHub Repository with ArgoCD
Creating an Argo CD Application
Manual Sync vs Auto Sync
Continuous Deployment using ArgoCD
CI/CD Demo: GitHub/Jenkins → Docker Image → GitOps Deployment with ArgoCD
Rollback and Drift Detection Overview
AI Assistant Usage for Argo CD Sync Errors, GitOps Flow and Manifest Review
Self Assessment

Module 14 — Monitoring and Observability

Monitoring Fundamentals & Observability Concepts
Prometheus Architecture & Data Model
Installing & Configuring Prometheus
Exporters (Node Exporter, Application Metrics)
Grafana Dashboards & Visualization
Metrics, Alerting & Alert Manager Setup
Logging Fundamentals & ELK Stack Overview
Centralized Logging (ELK / Loki)
Production Monitoring Strategy & SLOs
AI Assistant Usage for Log Analysis, PromQL Help, Alert Explanation and Incident Summaries
Self Assessment

Module 15 — Advanced DevOps and Production Scenarios

Project 1 — Cloud Infrastructure Automation

Project 2 — End-to-End CI/CD Pipeline Implementation

CI/CD Architecture Design (Real-world Scenario)
Source Control Integration (Git + GitHub)
Build Pipeline with Jenkins & Docker
Docker Image Build and Push
Implement Automated Testing & Pipeline Stages
Deploy Application to Kubernetes
Notifications, Rollbacks & Pipeline Optimization
End-to-End CI/CD Pipeline Implementation
AI Assistant Usage for Project Planning, Code Review, Troubleshooting and Documentation

Project 3 — GitOps Kubernetes Deployment

GitOps Project Architecture Design
Application Repository and Manifest Repository Setup
Docker Image Build and Push to Docker Hub / ECR
Kubernetes Manifests for Deployment, Service and Ingress
Installing and Configuring ArgoCD
Connecting Git Repository with ArgoCD
Creating ArgoCD Application for Kubernetes Deployment
Implementing Auto Sync and Self-Healing
Updating Application Version through Git Commit
Rollback and Drift Detection Demo
End-to-End GitOps Kubernetes Deployment Implementation
AI Assistant Usage for Project Planning, Code Review, Troubleshooting and Documentation

Capstone Project — Production DevOps System

Design Production-Ready DevOps Architecture
Multi-Tier Application Deployment on AWS
CI/CD Integration with Kubernetes (Jenkins / GitHub Actions)
Observability (Monitoring, Logging, Alerting)
Scaling, High Availability & Fault Tolerance
Security, Backup & Disaster Recovery
Performance Optimization & Cost Management
Final Capstone Project Submission
AI Assistant Usage for Project Planning, Code Review, Troubleshooting and Documentation

Career Support — Resume, LinkedIn, GitHub, Mock Interviews & Job Search

Student Ratings & Reviews

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About Course

What Will You Learn?

Course Content

Module 0 — Course Orientation, Tools Setup and Lab Environment

Program Introduction

Complete Course Roadmap

System Requirements and Lab Readiness

Lab Workflow, Learning Material Access, and Assignment Submission

Class Schedule, Guidelines, and Next Steps

Tools and Accounts Setup

AI Assistant Setup for DevOps Learning

Module 1 — DevOps Foundations and SDLC

Introduction to Software Development & Engineering Practices

SDLC Models (Waterfall vs Agile vs DevOps)

Why DevOps in Modern Engineering

DevOps Lifecycle & Key Stages

CI/CD Concepts & Workflow Overview

Monolith vs Microservices Architecture

DevOps Tools Ecosystem Overview

AI Assistant Usage for Understanding DevOps Concepts and Workflows

Self Assessment

Module 2 — AI-Assisted DevOps Overview

Introduction to AI in the DevOps Era

What Are LLMs and How They Work

Types of AI Assistants for DevOps

How AI Assistants Support DevOps Workflows

Use of Generative AI for DevOps Tasks

Overview: AI for Troubleshooting, CI/CD, Monitoring and Logs

Prompting Basics for DevOps

How to Verify AI-Generated Commands and Scripts

AI Hallucination Risks

Do Not Paste Secrets into AI Tools

Human Validation: AI as Assistant, Not Replacement

Module 3 — Linux Administration and Shell Scripting

Linux Architecture & Distributions Overview

File System Structure & Permissions Management

User & Group Management

Package Management (APT, YUM, DNF)

Process, Service & Systemctl Management

Networking Basics & SSH Configuration

Shell Scripting Fundamentals (Bash)

Variables, Loops & Conditional Statements in Scripts

Automating Tasks using Shell Scripts (Cron Jobs)

Linux Security Best Practices

AI Assistant Usage for Linux Commands, Shell Script Generation and Error Debugging

Self Assessment

Module 4 — Networking for Cloud and DevOps

Networking Fundamentals & Core Concepts

OSI vs TCP/IP Model

IP Addressing, CIDR & Subnetting

DNS, Name Resolution & Routing Basics

Ports, Protocols & Firewall Concepts

Load Balancing (L4 vs L7 Concepts)

AI Assistant Usage for Networking Command Output and Connectivity Troubleshooting

Self Assessment

Module 5 — Git and GitHub Version Control

Version Control Fundamentals & Git Concepts

Git Architecture & Workflow

Git Installation & Essential Commands (Clone, Commit, Push, Pull)

Branching Strategies (Feature, Release, Hotfix)

Merging & Handling Conflicts

GitHub Repositories & Collaboration Workflows

Pull Requests, Code Reviews & Team Collaboration

GitHub Actions Basics for CI Integration

Git Best Practices & Repository Management

AI Assistant Usage for Git Commands, Merge Conflicts and Pull Request Review

Self Assessment

Module 6 — AWS Cloud Infrastructure Core

Cloud Computing Fundamentals

AWS Global Infrastructure

IAM Users, Roles & Policies

EC2 Setup & Management

S3 Storage & Lifecycle Policies

VPC, Subnets & Routing

RDS Setup & Multi-AZ Design

DynamoDB & Use Cases

Amazon ECR for Container Images

Amazon ECS Overview

Amazon EKS Cluster Setup Overview

AWS CloudFormation Overview