The process of teaching an AI model to perform specific tasks by exposing it to data and optimising its parameters to minimise errors. Training involves feeding examples to the model and adjusting its internal values to improve performance over time.

Model training is a computationally intensive process that forms the foundation of machine learning system development. During training, models learn to recognise patterns by minimising the difference between their predictions and the actual outcomes (the “loss function”). Training methodologies vary based on the type of model and task, but typically involve dividing data into training, validation, and test sets to ensure the model generalises well to new data rather than merely memorising training examples.

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An enterprise training a customer churn prediction model by providing historical customer data including demographics, purchase history, service interactions, and whether customers remained active or left, allowing the model to identify patterns that predict future churn risk.