In the context of AI, what does the term "model training" refer to?

The term "model training" in AI refers to the process of teaching a model using data. This step is essential in the lifecycle of machine learning and artificial intelligence, as it involves using a dataset that contains input-output pairs to help the model learn the underlying patterns and relationships. Through training, the model adjusts its parameters to minimize errors in its predictions, enabling it to generalize and perform well on unseen data. This process requires an understanding of the data used, as well as the application of algorithms to optimize the model's performance during the training phase.

In contrast, deploying a model refers to the steps taken to make a trained model available for use in a production environment. Identifying anomalies relates to the evaluation and analysis of data to detect unusual patterns or outliers, which is typically an application of a model rather than the training itself. Gathering user feedback involves collecting input from end-users about the model's performance or suggestions for improvement, which may be used to inform future training cycles but is not part of the training process itself.