AI systems can be complex. Architecture patterns provide reusable blueprints to design them effectively, ensuring your AI solution is scalable, modular, and efficient. Here’s a breakdown of the most common AI architecture patterns.   Photo by Jan van der Wolf via Pexels Core AI system patterns. Pipeline pattern. This is great when you need clarity, control, and determinism in your AI system. It breaks a complex workflow into a clean sequence of stages: data ingestion → preprocessing → model training → inference Each step has a single, well‑defined responsibility. This structure makes debugging easier, improves replicability, and allows teams to optimise or swap out individual components without disrupting the entire system. In practice, it’s the backbone of many production ML and LLM workflows where consistency and traceability matter just as much as model performance.    Microservices pattern. Microservices takes AI architecture and breaks it into small, autonomous serv...