MLOps

Scaling AI to production requires a rigorous MLOps discipline. This category focuses on the lifecycle management of machine learning models, covering CI/CD pipelines for training, automated model versioning, and real-time performance monitoring. We discuss the use of Docker and Kubernetes for model serving, the implementation of feature stores, and the strategies required to maintain high-availability AI services in demanding enterprise environments. Share your experiences with monitoring for model drift and optimizing GPU cluster utilization.