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Insights on AI infrastructure, Decision Coherence, and building systems for the machine-driven era.

Feature Freshness Explained: Why Model Accuracy Drops in Production

Your model scored 94% in training. In production it's drifting toward 80%. The features you trained on don't match what the model sees at inference. Here's how to measure feature freshness, detect drift, and close the gap.

Boyd StoweJan 15, 20269 min read

Latest Articles

40 articles

Data Engineering

What Is Data Observability? The Complete Guide [2026]

Data observability monitors data health across your pipelines. Learn what data observability means, how it differs from data quality, the pillars of data observability, and why reactive monitoring isn't enough.

Feb 14, 202615 min read

AI & Machine Learning

The Decision-Time System Model

Kafka + ClickHouse solves streaming analytics—but not AI decision-making. Here's why teams searching for Kafka alternatives or a streaming database still hit walls: split state, temporal misalignment, and consistency gaps that break automated decisions.

Feb 14, 20269 min read

Architecture

What Is Derived Context?

Why data freshness matters for AI decisions: derived context is state computed from events that must be current at decision time. When feature freshness degrades, decisions fail—not from bad models, but stale context.

Feb 13, 202610 min read

Data Engineering

What Is a Data Contract? The Complete Guide to Data Contracts [2026]

A data contract defines the structure, format, and quality expectations for data exchanged between systems. Learn how to create, implement, and enforce data contracts across your data platform.

Feb 13, 202614 min read

Data Engineering

What Is Stale Data? How to Detect and Prevent Stale Data [2026]

Stale data silently breaks AI models, dashboards, and decisions. Learn what stale data means, the causes of data staleness, and how to detect and prevent it before it costs you.

Feb 12, 202612 min read

Architecture

Why Real-Time Decisions Fail: Incomplete, Stale, and Inconsistent Context

Stale data breaks AI decisions even when pipelines are fast. Fraud slips through, agents act on wrong memory, personalization fails—not from slow systems, but from data freshness gaps and context that's incomplete or inconsistent at decision time.

Feb 11, 202612 min read

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