What stage of the machine learning lifecycle involves making a trained model available for other applications?

The stage of the machine learning lifecycle that involves making a trained model available for other applications is known as model deployment. During this phase, the finalized model is integrated into a production environment where it can be accessed and utilized by different applications, services, or users. This process includes preparing the model to run in real-world scenarios, ensuring that it is scalable, efficient, and can handle incoming data effectively.

Model deployment is crucial because it enables organizations to leverage the insights provided by the model in practical settings, such as predictive analytics, automation of tasks, or decision support systems. It is a significant step that transforms theoretical understanding and trained capabilities into actionable tools that can produce value in everyday applications. This step also sets the stage for subsequent activities, like monitoring the model’s performance in the wild and retraining it as necessary.