A machine learning engineer needs to select a deployment strategy for a new machine learning application. The feature values are not available until the time of delivery, and results are needed exceedingly fast for one record at a time.
Which of the following deployment strategies can be used to meet these requirements?
The deployment strategy that can be used to meet the requirements of having feature values available only at the time of delivery and needing results exceedingly fast for one record at a time is Real-time. This strategy is designed for fast, on-the-fly processing of individual records as soon as the data is available, ensuring immediate results which is essential in scenarios where latency is critical.
If we want it ASAP and data arrives on the go it's real time. Streaming is used in real time (ex Spark Streaming & Auto loader). Would go with E
Real Time should be ...