AWS + DevOps with AI
45 live sessions from AWS fundamentals to a three-tier app running on EKS. Learn the core AWS services first, then the DevOps tools that run on top of them: Linux, Git, Docker, CI/CD, Terraform, Ansible, Kubernetes and monitoring. Every session pairs a live lab with the AI copilot engineers use for that tool.
What you will be able to do
Who it is for
Freshers, developers and IT professionals moving into DevOps or cloud roles.
Before you start
- A laptop with internet access
- No prior cloud or DevOps experience needed. The course starts from cloud basics and builds up.
Not a list of topics. Things you ship.
Each step produces something real, and each one feeds the next, all the way to the capstone.
- STEP 01 · Days 1 to 15
A three-tier AWS foundation
VPC, EC2 behind a load balancer, S3, IAM and CloudWatch wired together in the Day 15 lab.
- STEP 02 · Days 21 to 24
A containerized application
A multi-stage Dockerfile, Compose for several services, and a Docker Scout security audit.
- STEP 03 · Days 25 to 28
A CI/CD pipeline
GitHub Actions that build, test and push to ECR, then deploy to AWS with approvals and rollback.
- STEP 04 · Days 29 to 36
Infrastructure as code
Terraform modules with remote state, and Ansible roles for repeatable configuration.
- STEP 05 · Days 37 to 42
A Kubernetes deployment
Workloads on Amazon EKS packaged with Helm, plus a deliberately broken cluster you diagnose.
- STEP 06 · Days 43 to 45
Production capstone
A three-tier microservices app on EKS, observed with Datadog, Prometheus and Grafana.
45 lessons. 18 modules. Every day laid out.
Open a module, tick lessons as you go, and use the roadmap to jump anywhere. This is exactly what you will learn, in order.
Tick lessons below, press Play, or switch on Guided mode to see how a learning path unlocks step by step. Your ticks stay in this browser only.
- Completed
- In progress
- Not started
- Locked
- Hands-on lab
- Project
Roadmap
Modules and lessons
Phase 1: AWS Cloud Foundations
01IAM and account security0/2Days 1 to 2
- DAY 01
Cloud Fundamentals and IAM Basics
Cloud models, AWS global infrastructure, Free Tier, CLI and console setup, IAM users, groups, policies, MFA
Amazon Q Developer - DAY 02
IAM Advanced and Account Security
Roles, cross-account access, resource-based policies, IAM Identity Center, permission boundaries, root hardening
AI-assisted least-privilege policies
02Compute: EC2 and Auto Scaling0/2Days 3 to 4
- DAY 03
EC2 Fundamentals
Instance types, AMIs, key pairs, security groups, user data, EBS-backed vs instance store
Amazon Q for EC2 troubleshooting - DAY 04
EC2 Advanced, Auto Scaling and Storage
Launch templates, Auto Scaling Groups, ALB and NLB, placement groups, EBS, EFS, backups
AI scaling-policy recommendations
03Storage: S30/1Day 5
- DAY 05
S3, Fundamentals to Advanced
Buckets, versioning, bucket policies, static hosting, storage classes, lifecycle rules, replication
Claude and Amazon Q for bucket policies
04Networking: VPC, Route 53, CloudFront0/4Days 6 to 9
- DAY 06
VPC Fundamentals
CIDR planning, subnets, route tables, Internet Gateway, public vs private subnets
AI CIDR and subnet planning - DAY 07
VPC Advanced
NAT Gateway, peering, endpoints, Transit Gateway, NACLs vs security groups
Amazon Q reachability troubleshooting - DAY 08
Route 53
Hosted zones, record types, routing policies, health checks, domain registration
AI-generated DNS migration checklist - DAY 09
CloudFront
Distributions, origins, cache behaviors, Origin Access Control, signed URLs and cookies
AI cache-policy tuning
05Messaging and monitoring0/2Days 10 to 11
- DAY 10
SNS and SQS Messaging
Pub/sub, queues, fan-out pattern, dead-letter queues
Claude for event-driven design - DAY 11
CloudWatch
Metrics, logs, Logs Insights, alarms, dashboards, CloudWatch Agent
AI anomaly detection and alarm thresholds
06Serverless and events0/2Days 12 to 13
- DAY 12
Lambda, Fundamentals to Advanced
Function lifecycle, triggers, layers, VPC integration, Secrets Manager, cold starts, error handling
Claude Code for Lambda - DAY 13
EventBridge
Event buses, rules and patterns, Scheduler, cross-account and cross-region events
AI-generated event patterns
07Containers on AWS (overview)0/1Day 14
- DAY 14
ECR and ECS/EKS Overview
Registry, tagging, scanning, Fargate vs EC2 launch type, when to choose ECS or EKS
Docker Scout and Amazon Q
08AWS capstone lab0/1Day 15
- DAY 15
AWS Capstone Lab and Review
Mini-project: VPC, EC2 with ALB, S3, IAM and CloudWatch wired together, then a cost and security review
Hands-on lab Claude Code architecture review
Phase 2: DevOps Engineering
09Linux fundamentals0/3Days 16 to 18
- DAY 16
Linux Essentials and Shell Scripting
Filesystem hierarchy, users and permissions, package managers, bash scripting, cron
Claude and ChatGPT Codex for scripts - DAY 17
Linux Networking, SSH and Processes
Ports, SSH key auth, firewalls, process management, systemd services
AI firewall-rule generation - DAY 18
Linux Troubleshooting Lab
journalctl, /var/log, disk, CPU and memory diagnostics, a real incident drill
Hands-on lab AI log parsing and triage with Claude
10Git and GitHub collaboration0/2Days 19 to 20
- DAY 19
Git, Fundamentals to Advanced
Commit, branch, merge, conflicts, rebase vs merge, cherry-pick, stash, branching strategies
GitHub Copilot for commit messages - DAY 20
GitHub Collaboration
Pull requests, review etiquette, branch protection, CODEOWNERS
AI review comments and PR summaries
11Docker and containers0/4Days 21 to 24
- DAY 21
Docker Fundamentals
Containers vs VMs, Docker architecture, images vs containers, Docker Hub
- DAY 22
Dockerfile Mastery
Layers and caching, multi-stage builds, .dockerignore, image size optimization
Docker AI Agent (Gordon) - DAY 23
Docker Networking, Storage and Compose
Bridge, host and overlay networks, volumes vs bind mounts, multi-container apps with Compose
AI-generated docker-compose files - DAY 24
Docker AI and Security Lab
Docker Scout scanning, image hardening, best-practice audit of a real Dockerfile
Hands-on lab Gordon-assisted troubleshooting
12CI/CD with GitHub Actions0/3Days 25 to 27
- DAY 25
CI/CD Concepts and GitHub Actions Basics
Pipelines as code, workflows, jobs, steps, runners, triggers
GitHub Copilot for a first workflow - DAY 26
Advanced GitHub Actions and CI Pipeline
Matrix builds, secrets and environments, reusable workflows, build, test and push to ECR
AI secrets and permissions audit - DAY 27
CD Pipeline to AWS
Deploy to EC2 or ECS on merge, environment approvals, rollback strategy
AI rollback and canary patterns
13CI/CD with Jenkins (overview)0/1Day 28
- DAY 28
Jenkins Essentials
Architecture, freestyle vs declarative jobs, Jenkinsfile syntax, when employers still use Jenkins
AI Jenkinsfile generation
14Terraform0/5Days 29 to 33
- DAY 29
IaC Concepts and Terraform Basics
Why IaC, providers, resources, init, plan, apply, state file basics
- DAY 30
Variables, Outputs and Data Sources
Input variables, locals, outputs, data sources, conditional expressions
Claude Code scaffolding variables - DAY 31
Terraform Modules
Reusable modules, module composition, versioning
AI module generation - DAY 32
Remote State and Provisioning Real AWS Infra
S3 and DynamoDB backend, state locking, workspaces, VPC, EC2, S3 and IAM built in Terraform
AI-explained plan diffs - DAY 33
Terraform AI and Governance Lab
Policy-as-code concepts (OPA, Sentinel), cost estimation, an AI-reviewed module PR
Hands-on lab AI IaC code review
15Ansible0/3Days 34 to 36
- DAY 34
Ansible Fundamentals and Templating
Agentless architecture, inventory, ad-hoc commands, playbooks, Jinja2, handlers
AI-generated Jinja2 templates - DAY 35
Roles and Ansible Galaxy
Role structure, reusable roles, Galaxy, role dependencies
AI role scaffolding - DAY 36
Dynamic Inventory and Rolling Deploys
AWS EC2 dynamic inventory, rolling updates, serial deployment
Claude for debugging failed tasks
16Kubernetes and EKS0/6Days 37 to 42
- DAY 37
Kubernetes Architecture and Workloads
Control plane, nodes, kubectl, Pods, ReplicaSets, Deployments, rolling updates
kubectl-ai - DAY 38
Services, Networking and Config
ClusterIP, NodePort, LoadBalancer, Ingress, ConfigMaps, Secrets, volumes, PVCs
AI Ingress rule generation - DAY 39
Amazon EKS Deep-Dive
Managed control plane, node groups vs Fargate profiles, IRSA
Amazon Q for EKS troubleshooting - DAY 40
Helm
Charts, templating, values files, releases, upgrades and rollbacks
AI Helm chart scaffolding - DAY 41
Kubernetes AI Diagnostics Lab
Diagnose a deliberately broken cluster end to end
Hands-on lab K8sGPT and kubectl-ai - DAY 42
Scaling, RBAC and Troubleshooting
HPA, requests and limits, probes, RBAC, common failure patterns
AI RBAC review
17Monitoring: Datadog, Prometheus, Grafana0/2Days 43 to 44
- DAY 43
Datadog
Agent installation, dashboards, monitors and alerting, APM basics, log pipelines
Datadog Bits AI - DAY 44
Prometheus and Grafana
Prometheus architecture, PromQL, exporters, Grafana data sources, dashboards, alerting
AI PromQL and dashboard panels
18Final capstone0/1Day 45
- DAY 45
Capstone: Three-Tier App on AWS
Final integration, demo and an AI-assisted architecture review of your deployment
Capstone Claude Code, K8sGPT, Amazon Q
What you will use
AI copilots in this course
Ask AWS questions in plain English, generate IAM policies, troubleshoot EC2 and EKS
Debug Dockerfiles, optimize images, understand vulnerabilities
Natural-language diagnostics and kubectl commands on a broken cluster
Scaffold modules, explain terraform plan, generate and debug playbooks
Workflow YAML, secrets and permissions audits, rollback patterns
AI-assisted root-cause analysis across logs, metrics and traces
Three-tier microservices on AWS
What you build
- A frontend, backend API and database, containerized with Docker
- VPC, subnets, IAM roles, EKS, ECR, Route 53 and CloudFront provisioned with Terraform modules
- A GitHub Actions pipeline: build, test, scan, push to ECR and deploy to EKS with Helm
- Datadog for APM and logs, Prometheus and Grafana for cluster metrics
- An AI-assisted architecture review at the end
What you keep
- A working deployment on your own AWS account, or a recorded demo
- A GitHub repository with Terraform, Kubernetes and Helm manifests and the CI/CD workflow
- An architecture write-up you can use on your resume
- A live walkthrough of your design decisions
Taught by Vijay Kanth
DevOps and cloud engineer and trainer in Hyderabad. About Vijay
From learners
From zero Docker knowledge to deploying microservices on EKS in 8 weeks. Vijay's curriculum and real-project approach dramatically accelerated my learning.
Cleared AWS Solutions Architect and DevOps Engineer certs back to back. His mock sessions are harder than the actual exam, in the best way!
GitHub Actions CI/CD kit saved me days of debugging. The README is so clear I had the full pipeline running in 45 minutes. Great value.
Questions about this course
Do I need DevOps experience?
No. The first 15 sessions teach AWS and cloud fundamentals, so nothing is taught before its prerequisite exists.
How long is each session?
One hour of live teaching a day, five days a week, over about nine weeks. Each session ends with a short lab you complete on your own and review at the start of the next one.
What do I need to practice on?
A laptop and an AWS account. The labs use AWS Free Tier where possible, and Vijay explains cost control in the first session.
1:1 or batch?
Both are available. 1:1 follows your pace and goals. Talk to Vijay to pick the format that fits you.
Will I get a job after this?
No course can honestly promise a job. You will leave with hands-on skills, a capstone project you can show, and interview practice.
Ready to start?
Request a callback and Vijay will confirm the next batch, the format and the payment options.