Question

You maintain an Azure Machine Learning training pipeline that trains a model and then registers it. The business requires that the model is only registered when its validation accuracy exceeds 0.85; otherwise the pipeline should skip registration and finish successfully without failing. You want to implement this within the pipeline itself using components. What is the most appropriate approach?

Accepted Answer

Add a conditional control-flow node that evaluates the training component's accuracy output and only executes the registration component when the threshold is met.

Answer

Configure a data drift monitor on the training output so that registration is triggered only when drift stays below 0.85.

Answer

Set the registration component's retry policy to fail after the accuracy metric drops, causing Azure ML to skip that step.

Answer

Deploy the model to a batch endpoint first and let the endpoint reject models below 0.85 accuracy before registration.