Which of the following deployment paradigms can centrally compute predictions for a single record with exceedingly fast results?
Which of the following deployment paradigms can centrally compute predictions for a single record with exceedingly fast results?
Real-time deployment is designed to compute predictions for individual records with very low latency. This makes it ideal for applications that require immediate predictions, such as recommendation systems, fraud detection systems, and more. In a real-time deployment, the model is typically hosted on a server, and predictions are made on-demand for incoming data, ensuring rapid responses to requests.
The correct answer is E. Real-time. Real-time deployment is designed to compute predictions for individual records with very low latency. This makes it ideal for applications that require immediate predictions, such as recommendation systems, fraud detection systems, and more. In a real-time deployment, the model is typically hosted on a server, and predictions are made on-demand for incoming data.
E. Real-time.
Real-time deployment is designed for low-latency, high-throughput inference, making it suitable for scenarios where predictions need to be computed quickly for individual records. This paradigm ensures rapid responses to requests, allowing for fast results even for single records.