Which process involves a model producing immediate, real-time predictions as data is received?

The correct choice is associated with the continuous flow of data where models can generate predictions as soon as new data becomes available. This is fundamental for applications that require immediate insights or actions, making it a powerful tool in various industries such as finance, healthcare, and online services.

In this context, streaming predictions allow for a dynamic interaction between data inputs and model outputs, facilitating quick decision-making processes that can adapt to changing conditions or new information.

Understanding this process is vital for implementing effective AI solutions that rely on real-time data analysis, as it underpins the ability to provide timely responses to user actions or shifts in external environments.

Batch processing refers to handling data in groups or collections, where predictions are made after aggregating significant amounts of data, resulting in a lack of immediacy. Real-time processing could seem relevant, but it typically refers to systems capable of processing data as it comes in without specifically focusing on the prediction aspect. Delayed processing specifically indicates a postponement in analyzing data and generating insights, contrasting with the immediacy required for streaming predictions.