Have you ever wondered how companies build those impressive AI applications and keep them running reliably in production? In this video, I take a deep dive into MLOps, the discipline that makes it possible to continuously develop, deploy, and improve machine learning solutions at enterprise scale.
Many organizations build machine learning prototypes that never make it into production. MLOps provides the practices, culture, and architecture to bridge this gap.
What This Talk Covers # This presentation outlines how MLOps helps organizations move machine learning from isolated prototypes into reliable production systems. It frames MLOps as more than a technical setup: a mindset, culture, and set of practices that unify development and operations across the full ML lifecycle, including experimentation, training, deployment, serving, monitoring, and retraining.
