metodologia-data-science-architecture

What problem does it solve?

Data science architecture knowledge is often fragmented across silos, lacking a cohesive blueprint for end-to-end ML systems—from data ingestion and feature stores to model serving, MLOps, and governance.

Core Features & Use Cases

• Topology and data flow covering training, serving, monitoring, and governance.
• Feature store guidance with offline/online stores, registry, and point-in-time correctness.
• Experiment tracking and model registry with promotion workflows and lineage.
• Model serving architectures, deployment strategies, latency considerations, and monitoring.
• Governance and Responsible AI guidelines including bias checks, explainability, audit trails, and compliance mapping.

Quick Start

Generate the Data Science Architecture deliverable for a new enterprise ML project following the six sections and Mermaid diagram conventions.