nt import Client

@pipeline

def do_predictions():# model name and version are directly passed into client method

model = Client().get_model_version("iris_classifier", ModelStages.PRODUCTION)

inference_data = load_data()

predict(

# Here, we load in the `trained_model` from a trainer step

model=model.get_model_artifact("trained_model"),

data=inference_data,

In this case the evaluation of the actual artifact will happen only when the step is actually running.

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Last updated 19 days ago