Azure ML SDK v2: Registering Components via YAML

The objective is to register a machine learning component in Azure Machine Learning using the Python SDK v2. The scenario specifies that the component definition is stored locally at ./components/train/train.yml. To achieve this, two distinct steps are required: loading the component definition from the file and then creating or updating it in the workspace.

First, the code must load the YAML definition into an object that the SDK can understand. In Azure ML SDK v2, the function responsible for parsing a local YAML file containing a component definition is load_component. This function takes the path to the YAML file as its argument (e.g., path='./components/train/train.yml'). It returns a Component object representing the definition. Common distractors might include functions like create_component or register_component, but these do not handle the initial file parsing/loading step correctly in the context of the SDK v2 API structure.

Second, once the component object is loaded, it must be persisted in the Azure Machine Learning workspace. The correct method on the ml_client object to perform this action is create_or_update. This method accepts the component object and registers it, overwriting any existing version if necessary. Using a method like delete or get would clearly fail to achieve the goal of registration.

Therefore, the first blank should be filled with load_component to parse the YAML, and the second blank should be filled with create_or_update to persist the component in the workspace. This sequence ensures the local definition is correctly interpreted and then made available for pipeline use within the cloud environment.