A team is experimenting with traditional models for a classification workflow in Azure Machine Learning. The team requires a consistent way to manage assets that are created during experimentation. You need to ensure that artifacts can be reused and governed across projects. Which asset should you register?

During experimentation, a data science team produces trained model artifacts they want to share with the MLOps team for deployment, with the governance team for compliance, and with the QA team for testing. Registering the model in the Azure ML model registry gives each of these teams access to the versioned, immutable artifact through a single reference. Option B (Component) packages a reusable pipeline step, not the model output itself. Option C (Environment) captures the Python runtime, not the trained artifact. Option D (Pipeline) defines the orchestration workflow, not the resulting model. In Azure ML, the model registry is the governance store for trained model artifacts, recording who created it, when, from what data and code, and which metrics it achieved --- making it the asset to register for reuse and governance across projects. Microsoft Learn Reference Topic: Register and manage models in Azure Machine Learning -- Model registry