driftssupport
Driftsupport is a term used in data science and machine learning engineering to describe the set of practices, processes, and tools that monitor, measure, and mitigate drift in data distributions and predictive models in production systems. It covers both data drift, where input feature distributions change over time, and model or concept drift, where the relationship between inputs and targets evolves. The goal of driftsupport is to maintain model performance, reliability, and governance as environments change.
Detection and measurement in driftsupport rely on statistical methods and drift metrics. Common techniques include population
Operationally, driftsupport involves an end-to-end lifecycle: establish baselines, implement continuous monitoring, configure alerting and dashboards, and
Applications of driftsupport span finance, retail, healthcare, and other sectors reliant on stable, data-driven decision systems.