Supervised Learning
Training a model on labeled examples so it can predict labels for new data.
Supervised learning trains a model on data where every input is paired with a correct output label, so it learns a mapping it can apply to new, unlabeled inputs. The two core task types are classification (predicting a discrete category, like spam or not spam) and regression (predicting a continuous value, like a price). Amazon SageMaker supports this full workflow through built-in algorithms and custom scripts. The key exam distinction: supervised learning requires human-annotated labels before training, whereas unsupervised learning finds structure in unlabeled data. Scenarios mentioning “known outputs” or “historical examples with correct answers” signal supervised learning.
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