Databricks Machine Learning Associate Question 629
Single answerA retail company wants to implement a machine learning model to recommend products to users based on their browsing behavior. The recommendation system must provide suggestions within milliseconds after a user interacts with the website. Which model serving approach is the most appropriate for this use case?
- A
Batch serving
- B
Realtime serving
- C
Streaming serving
- D
Offline serving
Show answer and explanation
Correct answer: B
Explanation
Realtime serving is the most appropriate approach when low-latency responses are required, such as in the case of providing recommendations to users immediately after a website interaction. Batch serving and streaming serving have different use cases that do not prioritize response time for individual requests, making them unsuitable for this scenario.
- A. Incorrect.
Batch serving is not suitable for this use case because it processes data in bulk and typically introduces significant latency. It is designed for scenarios where real-time responses are not required.
- B. Correct.
Realtime serving is the correct choice for this use case because it provides low-latency responses, enabling the recommendation system to deliver suggestions in milliseconds after user interaction.
- C. Incorrect.
Streaming serving is not the optimal choice here as it is designed for continuous processing of data streams and is more suited for handling time-series data or event-driven pipelines rather than providing low-latency responses for individual requests.
- D. Incorrect.
Offline serving is not a valid model serving approach. It does not align with the requirements of responding to user interactions in real-time.