metodologia-ai-software-architecture

Document AI system architecture decisions by generating Architecture Decision Records (ADRs) that capture module boundaries, layer separation, and debt evolution plans across hardware, data, model, and inference layers.

Structure AI-enabled software architecture using a six-layer model: Hardware, Data, Model, Inference, Application, and Monitoring & Control. This defines scalable module boundaries and quality attributes for cross-functional teams.

Manage AI-specific technical debt by creating a debt evolution plan that tracks architecture decisions and monitors quality attributes like drift, explainability, and fairness throughout the ML pipeline lifecycle.

You need Architecture Decision Records when defining module boundaries, selecting architecture patterns, or evaluating ML pipeline architecture to ensure governance and align with regulatory needs like drift detection.

Yes, this framework aligns AI architecture with governance by evaluating quality attributes such as drift detection, explainability, and fairness, ensuring regulatory compliance across data, model, and application layers.

Evaluate quality attributes for AI pipeline architecture including drift detection, explainability, and fairness. These attributes ensure proper governance across the six architectural layers from data to monitoring.