Docker Decoded - Unstop Igniters Club SGU

A half-day technical session for 3rd-year engineering students, taking you from container basics to building AI-powered applications. Three sessions, each building on the last, delivered by practitioners working on these problems every day. No prior experience assumed.

10:00 – 11:00 · Introduction to Docker 101 ~ Raveendiran RR, Docker Inc.

"It works on my machine" is the oldest excuse in software, and containers are the reason it's finally retiring. This session starts from zero: what a container actually is, how it differs from a virtual machine, and what's happening under the hood when you run docker run. From there, Ravi walks through images and layers, writing your first Dockerfile, and using Docker Compose to run a multi-service application. Everything is demonstrated live.

You'll walk away knowing: the difference between an image and a container, how to containerize an application from scratch, how layers and caching affect build speed, and the everyday Docker commands you'll actually use.

11:00 – 12:00 · Claude Skills ~ Tejas Shah

AI assistants are useful out of the box, but they get genuinely powerful when you can teach them your own workflows. This session covers Claude Skills ~ reusable packages of instructions, scripts, and resources that an AI agent loads on demand to do specialised work. Tejas will walk through how Skills are structured, when a Skill is the right tool versus a prompt or an MCP server, and how to build one of your own.

You'll walk away knowing: how to author a Skill, how agents decide which capability to apply, and how to extend an AI assistant into a domain it knows nothing about.

12:00 – 1:00 PM · Docker and AI ~ Ajeet Singh Raina, Docker Inc.

Now put the two halves together. Every AI project eventually hits the same wall: dependency chaos, models that won't run locally, and agents with far more access to your machine than you'd like. This session shows how Docker has become the default way to build and ship AI applications, running models locally, wiring agents to real tools through MCP, and keeping an autonomous agent contained while it works. Closes with what this means for the kind of engineering roles you're about to walk into.

You'll walk away knowing: how to run an AI model in a container, how agents connect to tools and data, why sandboxing matters when an agent can execute code, and where to start building your own AI project.

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