In an industry where speed and security are paramount, this course stands out by introducing "Vibe Coding" and "Spec Mode" for infrastructure. You will move beyond writing YAML and HCL from scratch to using advanced AI assistants like Cursor and Kiro IDE to generate, validate, and secure your code. It acts as a professional bridge for DevOps and Platform Engineers who want to leverage Model Context Protocol (MCP) servers and AI-powered GitOps pipelines to deliver safer, compliant infrastructure at 10x speed.

This Course Offers

• AI-Assisted IaC Authoring: Master the use of GitHub Copilot and Cursor to write Terraform, Pulumi, and Ansible code through intelligent prompting and vibe-based workflows.
• Spec-Driven Development: Transition into structured "Spec Mode" with Kiro IDE to generate complex multi-cloud architectures from high-level technical requirements.
• Agentic Security & Compliance: Utilize MCP Servers and AI agents to automatically inject best practices, scan for vulnerabilities with Checkov, and enforce policies via OPA/Conftest.
• GitOps with AI Intelligence: Build automated pipelines that feature AI-powered PR reviews and automated remediation of infrastructure drift.
• Multi-Cloud Provisioning: Apply your skills across AWS and GCP, learning how to manage state, providers, and modules with AI assistance.
• 5 Real-World AIOps Projects: Complete a capstone project that provisions a secure, production-grade environment using a combination of Terraform, Ansible, and GitHub Actions.

Why We Love This Course

• It is strictly focused on the 2026 AI-First DevOps landscape, teaching you to use modern tools like Cursor and Kiro IDE that are currently reshaping how engineers work.
• The inclusion of MCP (Model Context Protocol) servers is a significant differentiator, allowing your AI tools to actually "understand" your cloud provider’s live documentation and best practices.
• It’s clear that the curriculum prioritizes "Security-as-Code," ensuring that while the AI helps you move fast, tools like tfsec and Checkov keep your infrastructure locked down.
• You walk away with a robust GitHub portfolio of AI-enhanced projects and a certificate of completion that signals your readiness for the next generation of SRE and DevOps roles.

The gap between a traditional sysadmin and a modern Platform Engineer is the ability to orchestrate intelligence. The question is whether you want to continue manually debugging HCL or finally master the AI workflows that automate the heavy lifting for you. This comprehensive guide provides the exact tactical roadmap you need to lead the future of cloud automation.

• DevOps and Platform Engineers looking to modernize their legacy IaC workflows with Generative AI tools.
• Site Reliability Engineers (SREs) seeking more secure, scalable, and automated infrastructure delivery pipelines.
• Cloud Architects who want to use AI to rapidly prototype and validate complex multi-tier application environments.
• Professionals preparing for technical interviews in AI-integrated DevOps and cloud engineering roles.

• Basic understanding of cloud computing and core DevOps concepts (like CI/CD).
• Prior experience with at least one scripting or programming language (Python, Bash, etc.).
• Familiarity with version control (Git) and a willingness to explore modern AI code editors (VSCode/Cursor).
• Access to an AWS Free Tier account to participate in the hands-on labs and capstone project.

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