Architecture Governance for AI/ML
Govern the architecture of ML pipelines, model serving infrastructure, and data processing systems
The AI/ML Challenge
AI/ML companies build systems with unique architectural challenges: training pipelines that process terabytes of data, model serving infrastructure that must maintain low-latency inference at scale, feature stores that bridge offline and online processing, and experiment tracking systems that manage thousands of model versions. The architecture of an ML system is its competitive moat — and it is often the least documented part of the organization.
Compliance & Regulatory
Key Capabilities
ML Pipeline Dependency Mapping
Map the complete dependency chain of training pipelines — data ingestion, feature engineering, model training, evaluation, and deployment. Identify SPOFs where a single component failure blocks the entire pipeline.
Model Serving Architecture Analysis
Analyze the structural dependencies of inference infrastructure — model loading, preprocessing, postprocessing, caching, and routing. Quantify the blast radius of changes to serving components.
Data Pipeline Structural Governance
Trace data flow through ETL/ELT pipelines, feature stores, and data warehouses. Identify coupling between data processing components that creates cascade failure risk.
EU AI Act Architectural Evidence
Map architectural findings to EU AI Act requirements for high-risk AI systems — transparency, data governance, technical documentation, and human oversight mechanisms.
Why AI/ML Teams Choose Axiom Refract
- ML pipeline failures are among the most expensive incidents in the industry — a broken training pipeline can cost weeks of compute time and delayed model releases
- The EU AI Act requires documented technical architecture for high-risk AI systems — automated evidence generation reduces compliance burden
- AI/ML company acquisitions require assessment of proprietary pipeline and model serving architecture — Axiom provides structural intelligence that accelerates due diligence