When building ML-driven internal tools and applications, data scientists may encounter bottlenecks upon trying to operationalize their models if engineering resources are not available. To remove these bottlenecks, Aqueduct has released an OSS platform that facilitates model deployment, integration with data and business systems, and observability. Aqueduct is designed around Workflows, which represent a sequence of data Artifacts (typically loaded from and persisted to databases) that are transformed by Operators. Workflows can be triggered on demand or scheduled.