Monitoring ensures AI systems continue to perform as intended after deployment, while drift refers to changes in data or environments that degrade accuracy and fairness. This episode introduces three forms of drift: data drift, where input distributions change; concept drift, where relationships between inputs and outputs shift; and label drift, where outcome distributions evolve. Learners explore why ongoing monitoring is essential for detecting these issues before they cause harm.
Examples demonstrate monitoring in practice. Credit scoring systems must detect drift during economic changes, healthcare models must adapt to evolving treatment protocols, and recommendation systems must adjust to seasonal behavior patterns. Tools such as dashboards, anomaly detectors, and drift metrics are explained alongside processes for human review and incident response. Challenges like alert fatigue and defining appropriate thresholds are acknowledged. By establishing structured monitoring and drift management, organizations ensure AI remains reliable, fair, and aligned with intended outcomes. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your certification path.