@DanKornas: AI infrastructure is too broad for random tutorials. AI Infrastructure Engineer Learning Path is a hands-on curriculum …

AI infrastructure is too broad for random tutorials. AI Infrastructure Engineer Learning Path is a hands-on curriculum for engineers who want a structured path into ML and LLM infrastructure. It helps you move from foundations to deployment by organizing the work into modules, labs, quizzes, and projects instead of sending you through scattered docs. Key features: • 10-module path – covers foundations, cloud, containers, Kubernetes, data pipelines, MLOps, GPU computing, observability, IaC, and LLM infrastructure • Hands-on labs – includes 62 labs across the modules with objectives, steps, validation, cleanup, and troubleshooting • Project track – includes model serving, an end-to-end MLOps pipeline, and an LLM deployment platform • Readiness support – prerequisite guidance helps you check Python, Linux, Git, ML, Docker, and Kubernetes basics first • Modern AI infra stack – touches tools and patterns like vLLM, RAG, vector databases, MLflow, Airflow, DVC, Prometheus, and Grafana It’s open-source (MIT license). Link in the reply