Crash Course: AI-Powered DevOps

Through high-stakes, real-world scenarios, you’ll plug AI directly into your infrastructure, documents, and cloud APIs—turning hours or days of manual effort into minutes. The course is designed to scale as new scenarios are added.

What you’ll learn:

• Why AI without context fails—and how to give AI secure, direct access to your systems
• How to build, tune, and evaluate a production-ready RAG knowledge hub
• How to automate cloud security workflows using Model Context Protocol (MCP) and live AWS APIs
• How to turn natural language into precise, auditable actions with real impact

Included scenarios:

• Production Ops (Kubernetes) Scenario: P1 outage during a major sales event. Generic chat AI guesses; an in-cluster troubleshooter connects via kubectl, finds a corrupted base64 secret, and restores service. Lab outcome: Diagnose and fix a broken deployment in minutes; understand why environment-aware AI crushes text-only suggestions.
• Knowledge and Compliance (RAG) Scenario: Documentation chaos threatens an audit. Unify scattered policies into a single, cited source of truth. Lab outcome: Boost retrieval accuracy from ~45% to 90%+ using better chunking, embeddings, reranking, and eval loops; eliminate “which doc is final?” risk.
• Cloud Security Automation (MCP + AWS) Scenario: A zero-day lands; leadership needs impact and fixes now. Manual correlation is too slow. Lab outcome: Build an automated security response engine that reads AWS docs, queries live resources, and scans IaC—compressing a multi-day audit into minutes.
• Agentic DevOps Team (Docker + Terraform): Build two focused helpers in Quen: a Docker Optimizer that shrinks large images with multistage builds, and a Terraform Security helper that finds and fixes risky IaC before deploy.
• AI Incident Commander: The commander agent ingests Plane tickets via MCP, separates Terraform drift from Kubernetes pod failures, and dispatches Cloud Architect and Kubernetes Specialist agents to apply fixes in parallel—restoring production in under five minutes.

Who it’s for:

• DevOps, SRE, Platform, and Cloud Engineers
• Security and Compliance teams
• AI/Automation practitioners bringing AI into real systems

Prerequisites:

• Command line, Git, and basic scripting
• Kubernetes and AWS fundamentals
• Access to a test cluster and a non-production AWS account for labs