What is the main use case for real-time inference in machine learning applications?

The primary use case for real-time inference in machine learning applications is to provide instant predictions with low latency. In many scenarios, businesses and applications require immediate responses based on incoming data. For instance, in domains such as finance for fraud detection, healthcare for diagnostic assistance, or e-commerce for personalized recommendations, it is crucial to quickly analyze user inputs or ongoing data streams. Real-time inference enables these applications to deliver timely and actionable insights, which can significantly enhance user experience and operational efficiency.

This emphasizes the importance of low latency, as any delay can lead to missed opportunities or a diminished quality of service. Characteristics of real-time inference include the ability to process requests rapidly and often in a streaming manner, which allows for continuous input and output in applications where timely decisions are paramount.