During the ML development lifecycle, a team experiments with different learning rates and numbers of hidden layers to maximize validation accuracy while keeping the training data fixed. Which pipeline component are they performing?
Model deployment
Hyperparameter tuning
Model monitoring
Data preprocessing
The scenario describes hyperparameter tuning. Hyperparameters-such as learning rate, number of hidden layers, or number of trees-control how a model is trained but are not learned from the data itself. Searching over different hyperparameter values while comparing validation metrics is the purpose of the tuning stage. Data preprocessing changes the input data, not the model settings. Model deployment moves a completed model into production, and model monitoring tracks performance after deployment; neither involves systematically varying hyperparameters.
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