In machine learning workflows, data scientists first build a model during the training phase and then use that model with unseen customer data. What is the specific term for this second step of producing outputs?
Data labeling
Inferencing
Hyperparameter tuning
Feature extraction
The step of applying a trained model to new, previously unseen data to generate predictions is called inferencing (or inference). Training is the earlier phase that adjusts model parameters, data labeling is a preparation activity for supervised learning, and hyperparameter tuning seeks better model configurations during training rather than during prediction time.
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