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Manage AWS IAM with Claude Code, Terraform and GitLab

Unlock the future of cloud security and automation. As cloud environments grow, managing AWS Identity and Access Management (IAM) manually becomes a bottleneck and a security risk.This hands-on course bridges the gap between traditional DevOps and modern AI assistance, teaching you how to build, secure, and deploy AWS IAM infrastructure at scale.

You will start with the fundamentals of AWS IAM before quickly diving into Claude Code, leveraging next-generation AI to accelerate your development workflow. From there, you will transition into practical Infrastructure as Code (IaC), using Claude Code to co-pilot the creation of robust Terraform configurations for IAM users, groups, and precise, least-privilege policies.

Finally, you will bring it all into a production-ready GitLab CI/CD pipeline. You won't just automate deployments—you will learn how to embed rigorous automated security gates directly into your pipeline to catch misconfigurations before they hit production.

What You Will Learn:

AI-Assisted IaC: Use Claude Code to write, refactor, and optimize Terraform.

AWS IAM Mastery: Design secure, scalable access controls.

DevSecOps Pipelines: Build GitLab CI/CD workflows with integrated security gates.

Ready to revolutionize your workflow? Bridge the gap between AI innovation and cloud security, and build pipelines that are fast, automated, and secure by design.

Build & deploy 8 microservices to production on AWS — Karpenter, GitOps, CI/CD, Observability + Resume Prep

What if you could deploy production-grade AWS infrastructure without writing a single line of code yourself?

That’s exactly what this course is about. You’ll take a real Spring Boot microservices application—eight services, real databases, real traffic—and push it all the way to production on AWS. Every Terraform module, every Kubernetes manifest, every CI/CD pipeline, and every runbook is generated by Claude Code. Your role is to think like an architect: write precise prompts, review the outputs, and make sure everything is production-ready.

This isn’t a step-by-step tutorial. It’s a project.

You step into the role of a DevOps engineer handed a Jira board and expected to deliver. You’ll work through real epics—networking, compute, container registry, databases, secrets, GitOps, observability—in the same sequence a real production team would follow.

What you’ll build:

A VPC with public subnets across multiple availability zones

An Amazon EKS cluster running cost-optimized Graviton ARM nodes

Amazon RDS MySQL for persistent storage

Amazon ECR with lifecycle policies and vulnerability scanning

A GitOps pipeline using ArgoCD (auto-sync for dev, manual approvals for production)

GitHub Actions CI pipelines that build, push, and trigger deployments

Secrets Manager integrated with External Secrets Operator for Kubernetes

A full observability stack with Prometheus, Grafana, Fluent Bit, and Zipkin

Why Claude Code?

AI doesn’t replace engineers—it amplifies them. But only if you know how to guide it, evaluate its output, and catch what it misses. This course focuses on building that skill in the context of a real-world project, so you walk away with both working infrastructure and a repeatable workflow.

By the end, you’ll have:

A production-ready AWS platform in your GitHub portfolio

Hands-on experience with Terraform, EKS, ArgoCD, and GitHub Actions

A repeatable, AI-assisted workflow you can apply to future projects

If you’ve been meaning to get serious about cloud infrastructure, this is where it starts.

“This course contains the use of artificial intelligence to give better experience on a better voice quality”

Course Overview

Master Claude Code, AI Subagents, MCP Servers, Slash Commands, Hooks, and GitHub Actions to build next-generation AI-augmented developer workflows. This course is your complete guide to the future of software engineering—where AI works alongside developers through automated pipelines, intelligent context handling, and  well-orchestrated subagent systems.

This hands-on program teaches you how Claude Code transforms development productivity by acting as an AI teammate capable of executing workflows, running MCP servers, generating code, reviewing API designs, maintaining memory, and performing GitHub-integrated automations.

Whether you’re a software engineer, DevOps professional, QA engineer, or an AI enthusiast, this course equips you with the skills to build enterprise-grade AI workflows using Claude Code’s cutting-edge capabilities.

Learning Objectives

By the end of this course, students will be able to:

Understand Claude Code: Explain how Claude Code functions as both an MCP server and client, and its role in AI-assisted development

Implement Subagent Systems: Design and deploy specialized AI subagents for different aspects of the development lifecycle. We will go over understanding how Subagents work and what are their benefits

Slash Commands: Developers trigger powerful coding, debugging, and automation actions directly from chat. They streamline workflows by invoking predefined tools, hooks, and GitHub Action integrations with a simple command.

Workflow: An automated, multi-step process that Claude can run on your codebase—such as generating code, refactoring, testing, or syncing changes—based on defined steps in your project configuration. It acts like a programmable pipeline that Claude executes deterministically, ensuring repeatable and reliable automation triggered by slash commands or file changes.

Configure MCP Servers: We will take a deep dive on MCP server. Set up and integrate Model Context Protocol servers to extend Claude Code's capabilities. We will integrate with the MCP servers

Memory Systems: Create and maintain hierarchical memory structures that enhance AI assistance quality

Apply Best Practices: Implement enterprise-ready workflows using AI subagents while maintaining security and compliance standards

SECTION 1 — Introduction

You’ll start with a high-level view of large language models and how Claude Code builds on them to act as both an MCP client and server. Learn installation, extension setup, and create your first “Hello World” application inside the IDE.

SECTION 2 & 3 — Slash Commands

Understand Slash Command structure, frontmatter definitions, and backend logic.

Learn how to create custom commands powered by Bash scripts to automate:

Code refactoring

Git commits & version control

MCP integration

Multi-step workflows

Hands-on labs reinforce practical usage.

SECTION 4 — Subagents: General Purpose

Dive deep into the Subagent

You’ll build intelligent multi-agent systems that perform tasks like:

Travel planning

Trip scheduling

Restaurant recommendations

Parallel & sequential orchestration

These modules teach AI planning and agent chaining.

SECTION 5 — Subagents for Developers

Use Subagents to accelerate real engineering workflows:

API review assistants, documentation reviewers, code auditors, and more.

Perfect for teams adopting AI pair-programming at scale.

SECTION 6 — Workflows with Subagents + Slash Commands

Build hybrid pipelines combining Slash Commands and Subagents.

Create deterministic workflows that Claude executes reliably for:

Code changes

Commit flows

Automated testing

Structured build steps

SECTION 7 & 8 — Claude Code Memory (Theory + Hands-On)

Master Claude’s hierarchical memory system, including:

Context preservation

User memory

Project root memory

Subdirectory memory

Memory priority

Memory access commands

This section teaches persistent multi-file context handling—critical for large projects.

SECTION 9 — All About MCP

A full deep dive into Model Context Protocol, covering:

MCP

Server components

Transports

End-to-end data flow

You will clearly understand how tools communicate with Claude.

SECTION 10 — Claude Code with MCP Servers

Integrate powerful servers:

Puppeteer MCP

Sequential Thinking MCP

GitHub MCP (with and without authentication)

Learn how MCP extends Claude Code into a full automation platform.

SECTION 11 — Hooks

Type Script formatting

Activity logging

Execution safeguards

Multi-step sequences

Debugging failing Hooks

SECTION 12 — Claude Code with GitHub Actions

Pull Request creation

Bug fixing

Code review workflows

Repo updates

Continuous automation pipelines