Contributed to the mlflow/mlflow repository by adding support for Databricks Lakebase as a resource type within the MLflow model serving framework. Developed a new Databricks Lakebase class in Python and integrated it into the existing resource management flow, enabling users to specify and manage Lakebase instances as model dependencies. This work involved API development and MLOps practices to ensure seamless deployment workflows for models backed by Lakebase. The integration allows MLflow users to define, deploy, and serve models with Lakebase resources, enhancing flexibility in model management. No bug fixes were recorded during this period, with focus on feature delivery.