MLOps is a systemic approach to AI model life cycle management, which involves monitoring a machine learning model throughout its lifecycle and governing everything from underlying data to the effectiveness of a production system that relies on a particular model. It is necessary for building, maintaining, and deploying AI applications at scale. Key components of MLOps include continuous integration/continuous delivery (CI/CD), model development environments (MDE), champion-challenger testing, model versioning, model store and rollback. Milvus is an open-source vector data management platform that supports the transition to Software 2.0 and manages model life cycles with MLOps.