Evaluation
Data Drift
When the real-world data your AI encounters starts to look different from what it was trained on, making it less accurate.
Definition
A gradual shift in the statistical properties of input data over time, causing a deployed model's predictions to degrade. Can result from seasonal changes, market shifts, or evolving user behavior.
Why it matters
The silent killer of production AI, models that were accurate at launch can quietly become unreliable.
Where Sophizo applies this
Sophizo deploys Data Drift inside revenue and AI engagements with growth-stage operators and PE-backed portfolios.
See ForecastIQ →Related terms in Evaluation
Agent Evals
Standardized tests for AI agents to prove they are smart, safe, and reliable before they are deployed.
AI Model Monitoring
Keeping a constant watch on a deployed AI to make sure it hasn't gotten broken or less accurate over time.
Area Under the Curve (AUC)
A score from 0 to 1 that tells you how good your model is at distinguishing between two things (like spam vs. not spam).
Conformal Prediction
A technique that tells you not just what the AI predicts, but how confident it is, with a mathematical guarantee.
From vocabulary to outcomes
Ready to put Data Drift to work?
Knowing the term is step one. Deploying it inside a revenue architecture that compounds is what Sophizo builds.
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