Abstract

The paper addresses the entry of DevOps into lakehouse platforms to lessen the deployment and administration of machine learning models. It intends to discover the successful practices that achieve faster deployments, better operational efficiency, and strong management of data-driven applications without technical jargon. Streamlining processes and increasing collaboration across development and operations teams take DevOps miles ahead in adaptability and efficiency with lakehouse platforms. The paper includes pragmatic implementations and transformational potential between these hybrid data ecosystems through continuous integration, deployment, and automated monitoring. The results will emphasize how such integration would allow a more dynamic and responsive data management strategy designed for innovation and success.