AI & Data
Running AI at enterprise scale: the operational infrastructure beyond model training
Enterprise AI programs that reach production consistently solve the same set of operational problems: model versioning, feature store management, inference infrastructure, and drift monitoring. This paper documents the architecture for each.
The operational problems that determine AI program success
Enterprise AI programs that reach production consistently solve the same set of operational problems. The programs that don't reach production consistently fail at the same set of problems. The difference between the two groups is not model quality — it's operational infrastructure. The four problems that determine production readiness are: feature consistency between training and inference; model versioning with reproducible training pipelines; inference infrastructure that meets latency and availability requirements; and drift monitoring that detects degradation before it affects business outcomes.
Feature store architecture for enterprise AI
A feature store is the infrastructure that computes, stores, and serves the features that machine learning models are trained on and make predictions with. The critical architectural requirement is that the same feature values used during training are used during inference — training/serving skew is the most common cause of model performance degradation in production. An enterprise feature store requires: an offline store for batch feature computation and training data generation; an online store for low-latency feature serving (typically < 10ms P99); a feature registry that documents feature definitions, lineage, and ownership; and a monitoring layer that detects feature distribution drift and serves as an early warning system for model degradation.
Inference infrastructure: latency, throughput, and cost
The inference infrastructure requirements for enterprise AI vary dramatically by use case. Real-time fraud detection requires < 50ms end-to-end latency with 99.99% availability and the ability to handle transaction volume spikes of 10–100x normal traffic. Batch scoring pipelines for risk models require high throughput with cost efficiency as the primary constraint. The architectural decision that determines inference infrastructure cost is the split between CPU inference (cost-efficient, higher latency) and GPU inference (lower latency, 10–30x higher cost). Most enterprise use cases can be served with CPU inference if the model architecture is optimised for inference efficiency — a step that is frequently skipped in enterprise AI programs.
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Paper details
Authors
Priya Nair, Head of Data Engineering
Marcus Webb, AI Infrastructure Lead
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