Roadmap for to master in AI

john abraham

Start by building the right foundations, then work your way up to deploying and optimizing AI systems.  → Linux systems, networking basics, and Python scripting → Docker containers and Kubernetes orchestration → LLM fundamentals and ML pipelines → GPU architecture and LLM deployment → Inference optimization, AI networking, and observability  Learn these in the right order to understand not just how AI models work, but how to run them reliably in production. Save this roadmap and use it to plan your AI engineering journey.  . .  [AI engineering roadmap, AI engineer, Linux, networking, Python scripting, Docker, Kubernetes, LLM fundamentals, ML pipelines, GPU architecture, LLM deployment, inference optimization, AI networking, AI observability]

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00:00LLM fundamentals, I'd put that at number six, networking basics, that's an easy number two, AI networking, that goes straight

00:07to number 11, docker containers, number four, Linux systems, that's definitely number one, LLM deployment, I'd put that at number

00:14nine, ML pipelines, that's seven, AI observability, that's number 12 for sure, Python scripting, that's definitely number three, GPU architecture,

00:23number eight, Kubernetes orchestration, I'd put that at number five, inference optimization, that's a solid number 10.

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