DevOps & Platform Engineering with AI

Detailed Curriculum · 160 hours · 120 traditional + 40 AI-augmented

Prerequisites: you don't need a computer science degree or years on the job — if you can write basic code in any backend language (C#, Java, Python, Go, and TypeScript all work fine), have poked around a cloud console before, and know your way around Git, you're ready to start. No prior AI or LLM experience needed — that's exactly what this course teaches you. 

Positioning:
In this course, DevOps and Platform Engineering are not treated as two separate areas of expertise, but as two complementary parts of a holistic engineering approach required to take software from commit to production and operate it reliably at scale.

It covers the full breadth of both source courses — pipelines and infrastructure, containers and service mesh, cloud operations, platform-as-product and internal developer platforms, data governance, FinOps, and reliability at scale — at roughly half the depth per topic that two full standalone courses allowed: every major concept from both originals survives, each with its own hands-on lab, but the lab time budgets, case-study scope, and optional detours are compressed to fit one 160-hour arc instead of two. It closes with the same structural payoff both source courses shared: 40 hours of AI-augmented DevOps and platform engineering, showing exactly where agentic automation earns a place in the pipeline and the platform — and where a human approval gate stays non-negotiable.