Modern data and machine learning (ML) platforms have become critical infrastructure for enterprise decision‐making, yet many organizations continue to struggle with unreliable pipelines, poor data quality, fragmented feature engineering, and models whose behavior is difficult to explain or govern. While substantial progress has been made in data platform and MLOps tooling, most systems remain organized around technical layers rather than business domains, leading to unclear ownership, semantic drift, and fragile governance processes. This paper presents a domain‐driven architecture for data and AI platforms that treats data products, pipelines, features, and models as first‐class domain artifacts. The approach applies domain‐driven analysis to the entire analytics and ML lifecycle, enabling governance, quality enforcement, and lineage by construction rather than by after‐the‐fact controls. We introduce the core system model, describe the key architectural components, and present a reference architecture for large‐scale enterprise environments. The approach is illustrated using an end‐to‐end enterprise analytics and AI platform scenario. The result is a platform that is not only scalable but also reliable, explainable, and governable.
Akshay Sharma (2026) studied this question.