What is the process of deploying a machine learning model into a real-world environment for live predictions called?

The process of deploying a machine learning model into a real-world environment for live predictions is referred to as operationalization. This term encapsulates not just the technical aspects of deploying the model but also emphasizes preparing it for ongoing use, ensuring it can perform in production settings, and being able to handle real-time data inputs effectively.

Operationalization includes several critical activities such as setting up the necessary infrastructure, monitoring model performance, ensuring scalability, and enabling ease of access for stakeholders who utilize the model's predictions. The focus is on making the model operational so that it can continuously deliver value in a production environment.

While implementation might seem relevant, it generally refers to the broader act of putting a system in place, which may or may not include considerations for maintenance and performance in a live environment. Integration relates primarily to how the model fits into existing systems or workflows but doesn't encompass the full scope of deploying for live predictions. Validation, on the other hand, is the process of assessing the model’s accuracy and reliability before deployment, rather than the act of deploying it itself. Thus, operationalization specifically captures the essence of preparing and executing the live deployment of a machine learning model.