Before Watson OpenScale can monitor models hosted on an external ML platform such as Amazon SageMaker, what initial step must be completed inside Watson OpenScale?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Think of it this way: in real-world AI governance, register the external ml platform as a machine learning provider in openscale. is exactly what teams reach for when they need to handle this scenario. Watson OpenScale supports multi-cloud monitoring by allowing administrators to register external ML platforms as machine learning providers within its configuration. On the exam, remember that this falls squarely under the 3.0 Configure watsonx.governance domain.
Full explanation below image
Full Explanation
Watson OpenScale supports multi-cloud monitoring by allowing administrators to register external ML platforms as machine learning providers within its configuration. This registration establishes the connection credentials and endpoint details that OpenScale needs to retrieve scoring data from the external platform. Only after the provider is registered can subscriptions be created for models hosted on that platform. The correct answer, "Register the external ML platform as a machine learning provider in OpenScale.", directly addresses the scenario described because it aligns with the specific governance requirement in question. The incorrect options ("Export the SageMaker model artifact and re-deploy it on Watson Machine Learning.", "Configure a cross-account IAM trust policy between AWS and IBM Cloud.", "Install the OpenScale Python client library directly on the SageMaker endpoint.") may seem plausible but do not satisfy the core requirement. Understanding the distinction between these concepts is critical for IBM watsonx.governance implementations and is frequently tested in the 3.0 Configure watsonx.governance section of the certification exam.