ai-infra-engineer-learning

Why we included this project

Platform and DevOps engineers shifting into infrastructure work will find this one of the more complete self-guided tracks around, and MLOps engineers wanting to go deeper will too. The roughly 500-hour curriculum walks through building systems from the ground up: Docker and Kubernetes, GPU cluster setup, distributed training, plus the monitoring and cost work that keeps production alive. Instead of reading about it, you implement modules with code stubs and TODO comments, and the projects look like real deliverables you'd be asked to produce. It assumes a few years of experience, so it skips beginner hand-holding and gets straight to auto-scaling, vLLM-based LLM serving, RAG pipelines, and observability. For an individual mapping their own career path or a team building internal enablement, it's a practical roadmap with working examples to follow.