In this article, we continue from Part 1, where we began presenting a range of AI architecture patterns that function as reusable blueprints for effectively designing AI solutions. This can support you on your journey in identifying the appropriate one (or ones) for the problem you are trying to solve. Photo by Jan van der Wolf from Pexels Data centric patterns. Feature store pattern. This one has become a foundational element of mature ML platforms because it centralises the entire feature engineering lifecycle, ensuring that models train and serve on consistent, high‑quality data. Instead of letting teams repeatedly rebuild the same user metrics, behavioural aggregates, or time slot signals, a feature store provides a single source of truth where these features are computed once, documented, versioned, and reused across many models. This shared infrastructure: Eliminates training‑serving skew, which is one of the most common causes of silent model degradation. Enables organisat...
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...