I’m building and maintaining an open-source, zero-to-production AI engineering curriculum aimed at helping developers go from math foundations to shipping real AI systems. I’m the creator and maintainer of the repo below and am using it as a living, versioned path that I update in public with real-world lessons from building agents, tools, and infra. The curriculum is organized into phases (setup, math, ML fundamentals, then agents and production), and each lesson must end in a reusable artifact: a small library, tool, agent, or service-ready component rather than just a notebook. Technically, I focus on reproducible environments (Docker, pinned deps, task runners), basic evaluation harnesses (baselines, metrics, latency/resource checks), and realistic integration patterns (API contracts, retries, logging, and observability hooks) so the same code can move from laptop to server with minimal changes. Current limitations: deep learning, distributed training, and advanced inference optimization are only lightly touched so far and are planned for upcoming phases as I stabilize the foundations. Repo (open source): https://github.com/rohitg00/ai-engineering-from-scratch submitted by /u/SeveralSeat2176
Originally posted by u/SeveralSeat2176 on r/ArtificialInteligence