Architectural Foundations of MLOps, AIOps, and LLMOps

The article presents a production blueprint for modern AI systems, arguing that training a model is only 30% of the journey while the real challenge lies in MLOps, AIOps, and LLMOps architecture. It covers Docker-based dependency isolation, multi-service orchestration with docker-compose, and environment variable management as foundational practices for deploying reproducible, observable, and scalable ML systems in production.