Production ML Monitoring - Outliers, Drift, Explainers & Statistical Performance

This talk presents practical patterns for monitoring machine learning models in production, applying microservice monitoring and statistical techniques to deployed models. A hands-on example trains and deploys an image classifier, then adds monitoring components such as explainers, outlier and drift detectors, and architectural patterns for scaling across models.