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A data scientist is experimenting with model training. After some iterations, one model seems suitable for deployment but isn't quite ready for consumption yet. An Azure Pipeline is created to train the model. Which environment should the model be trained in first?
How can a machine learning engineer control the movement of a model from development to pre-production?
Add a trigger.
Add an approval check.
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