What Drift (data + model) means in practice
Drift is the operational reality that models trained on historical data degrade as the real world they predict on shifts. Data drift is the input distribution moving — new product categories, new customer behaviours, new document formats. Concept drift is the relationship between inputs and labels shifting — fraud patterns evolve, customer-support intents change as a product evolves. Production ML systems need drift detection: monitor input distributions, hold out a periodic labelled sample for accuracy measurement, alert when either crosses a threshold. The remediation is rarely "retrain from scratch" — usually it is targeted retraining on the recent data, or a prompt update for a generative system.
Related terms
MLOps →
The discipline of taking ML and AI models from development through to reliable production operation — versioning, deployment, monitoring, evaluation, governance.
Evaluation (evals) →
Systematic testing of an AI system against a curated set of inputs to measure quality on the dimensions the business cares about — accuracy, faithfulness, safety, format.
LLM observability →
Capturing every LLM call (prompt, retrieved context, response, latency, cost, user feedback) in a structured store so production behaviour can be inspected and improved.
All 62 terms, in plain language
Sovereign AI, RAG, agentic AI, IDP, MLOps and the regulations that shape enterprise AI.