Question

You are an ML engineer at a logistics company. A data scientist has trained a model and logged it with MLflow, and you now need to deploy it to a managed online endpoint for real-time scoring. The endpoint must serve two model versions simultaneously so you can compare responses before committing. You want to minimize the amount of custom code required for the deployment. What should you do?

Accepted Answer

Deploy each model version as a separate deployment under the same managed online endpoint, relying on MLflow's no-code deployment since the model has a logged signature.

Answer

Create two separate managed online endpoints, one per model version, and configure a load balancer to route between them.

Answer

Author a custom scoring script and environment for each version and deploy both as batch endpoints under a shared batch deployment.

Answer

Register both models in a workspace registry and invoke them directly from the registry without creating any endpoint.