Building machine learning systems that work in production is significantly more complex than training high-accuracy models in research. This social aims to bring together researchers, engineers, and practitioners interested in MLOps—the set of practices that enables scalable, reproducible, and reliable ML deployment. We will explore the challenges of operationalizing ML, from data drift and CI/CD to model monitoring and governance. The session will include lightning talks, informal discussion circles, and networking opportunities. It is targeted at attendees who want to bridge the gap between cutting-edge ML research and real-world system deployment.