Ai Devops: Automate Aws With Terraform & Claude Code
Published 4/2026
Created by Kostiantyn Skrypnyk
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 36 Lectures ( 3h 47m ) | Size: 2.2 GB
What you'll learn
✓ Build AWS infrastructure using Terraform with AI assistance (Claude Code)
✓ Generate, review, and improve Terraform code using AI workflows
✓ Set up and use MCP for Terraform + AWS automation
✓ Design safe AI deployment pipelines with guardrails and validations
✓ Automate infrastructure workflows using AI hooks, skills, and subagents
✓ Integrate AI-powered reviews and automation into GitHub Actions
✓ Analyze infrastructure for security, cost, and best practices using AI tools
Requirements
● Basic understanding of IT concepts (cloud, servers, networking)
Description
This course is designed for DevOps engineers, cloud engineers, and architects who want to build modern AI-powered infrastructure workflows using Terraform, AWS, GitHub and Claude Code.
Today, infrastructure is no longer just written manually. The industry is shifting toward AI-assisted development, where engineers generate, review, and manage infrastructure using intelligent tools, while still keeping full control and safety.
In this course, you will learn how to combine Terraform, AWS, and AI (Claude Code) to build real-world DevOps workflows that are faster, safer, and more scalable.
This course follows a practical, step-by-step approach, from environment setup to advanced automation patterns. Every concept is implemented hands-on, not just explained.
What you'll learn in this course
• How to use AI (Claude Code) to generate and improve Terraform code
• How to structure and control AI output using configuration files
• Terraform fundamentals and real-world infrastructure patterns
• AWS basics: IAM, S3, EC2, networking, and security groups
• Setting up Terraform remote state and safe deployment workflows
• MCP (Model Context Protocol) for integrating AI with Terraform and AWS
• Building reusable AI workflows using hooks and skills
• Designing subagents for Terraform implementation and review
• Applying guardrails to prevent unsafe infrastructure changes
• Automating infrastructure checks (security, cost, best practices)
• Integrating AI into GitHub Actions for automated PR reviews and deployments
What makes this course different
This course focuses on how AI is actually used in real DevOps workflows, not just demos or simple prompt examples.
You will build a complete AI-assisted infrastructure pipeline, including
• Code generation
• Validation and review
• Safe deployment
• Automation with GitHub
By the end, you will have a working setup that you can reuse in real projects or your company environment.
Key Features
• Step-by-step, hands-on implementation
• Real-world DevOps workflows, not simplified demos
• Clear and structured explanations
• Production-oriented approach
• Focus on safety, automation, and scalability
Requirements
• Basic understanding of IT or cloud concepts
• No prior AWS, Terraform or AI experience required
Why take this course
By taking this course, you will learn how to work faster with AI while keeping full control over your infrastructure.
Instead of replacing your skills, AI will become your assistant - helping you write Terraform, review changes, and automate workflows.
You will gain the ability to design AI-powered DevOps pipelines that reflect how modern engineering teams are evolving today.
This course is ideal for engineers who want to stay ahead of the industry, reduce repetitive work, and build production-ready infrastructure using the latest tools.
Who this course is for
■ DevOps engineers who want to integrate AI into their workflows
■ Cloud engineers working with AWS and Terraform
■ Developers who want to automate infrastructure using AI
■ Engineers curious about AI-powered automation and tooling
■ Anyone who wants to build real-world infrastructure faster with AI
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