What does the term 'model deployment' refer to in machine learning?

The term 'model deployment' in machine learning specifically refers to the migration of a model to a production environment. This process involves taking a trained model and making it available for use in real-world applications, where it can be accessed by end-users or other systems to make predictions or derive insights from new, incoming data.

Deploying a model is crucial because it transforms the theoretical knowledge gained during model training into practical applications that benefit businesses and users. This process encompasses several key steps, including ensuring the model is appropriately configured for the production environment, monitoring its performance, and possibly integrating it with other applications or systems for data input and output.

In contrast, the other options focus on different aspects of the machine learning lifecycle. For instance, training the model refers to the phase where the model learns from data, gathering training data involves preparing the dataset for training, and collecting user feedback pertains to evaluating the model's performance and user satisfaction post-deployment. Thus, 'model deployment' is distinct in its proactive role in making machine learning models functional in practical scenarios.