A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring. The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3. The company needs to use the central model registry to manage different versions of models in the application. Which action will meet this requirement with the LEAST operational overhead?
I figured D since unique tags seem like a quick way to handle versions per model, especially if you just care about simple tracking and not strict organization. Not sure if it's the cleanest for larger setups but it does the job. If I'm off, let me know.
Yeah, C makes sense since SageMaker Model Registry with model groups is specifically built for managing versions centrally. Tags in D can help for custom metadata, but they don't organize versions as cleanly or automatically. I think C keeps things simple and integrates better with SageMaker's ML workflow out of the box. Let me know if I'm missing a nuance, but pretty confident it's C.