Databricks Machine Learning Professional Question 215
Single answerYou are tasked with deploying a machine learning model for real-time inference in a production environment. The solution must scale seamlessly with fluctuating traffic, provide high availability, and be easily integrated into other systems via APIs. Why would using a cloud-provided RESTful service deployed in containers be the most appropriate approach?
- A
Cloud-provided RESTful services in containers automatically handle scaling and load balancing, making them ideal for handling varying traffic patterns.
- B
Using cloud-provided RESTful services in containers guarantees that the model will have zero latency for all inference requests.
- C
Containers allow the model to be packaged with its dependencies, ensuring consistency across development, testing, and production environments.
- D
Cloud-provided RESTful services in containers are designed to handle batch processing workloads efficiently and are not suitable for real-time inference.
Show answer and explanation
Correct answer: A
Explanation
Cloud-provided RESTful services in containers are ideal for real-time production deployments because they offer seamless scaling, high availability, and easy integration with other systems. These services are designed to handle fluctuating traffic and ensure reliable performance, making them highly suitable for production-grade machine learning use cases.
- A. Correct.
Correct: Cloud-provided RESTful services combined with containers enable seamless scaling and load balancing, ensuring the deployment can handle varying traffic patterns while maintaining availability.
- B. Incorrect.
Incorrect: While cloud-provided services aim to minimize latency, they cannot guarantee zero latency due to network variations and computation time.
- C. Incorrect.
Incorrect: While it is true that containers ensure consistency across environments, this alone does not justify their use for real-time inference. The question requires focusing on real-time production-grade needs.
- D. Incorrect.
Incorrect: This is a misleading statement. Cloud-provided RESTful services in containers are highly suitable for real-time inference and are not restricted to batch processing workloads.