Understanding how to monitor AI model drift in production is essential for teams deploying machine learning systems at scale. Model drift occurs when the statistical properties of input data shift over time, causing prediction accuracy to degrade silently while systems continue to run. This resource covers the mechanisms behind drift detection, including data distribution shifts, concept drift, and prediction decay patterns that threaten model reliability. Organizations that fail to catch drift early face compounding performance losses, increased false positives, and customer-facing failures that damage trust. Teams managing both traditional ML and LLM deployments will find tactical approaches to implement continuous monitoring without excessive overhead.