MLS-C01 Question 297
Single answerA retail company wants to implement a personalized product recommendation system on their e-commerce platform. They have limited ML expertise but want to leverage AWS services to quickly deploy a solution. Which AWS service should they use to build this recommendation system with minimal effort?
- A
Amazon SageMaker
- B
AWS DeepRacer
- C
Amazon Personalize
- D
Amazon Rekognition
Show answer and explanation
Correct answer: C
Explanation
Amazon Personalize is the correct choice because it is a fully managed service designed for creating personalized recommendations with minimal ML expertise. It abstracts the complexities of building and training recommendation models, allowing the retail company to implement a recommendation system quickly and efficiently.
- A. Incorrect.
Amazon SageMaker is a powerful service for building, training, and deploying machine learning models, but it requires a deeper ML knowledge and effort to create a recommendation system from scratch. It is not the most suitable choice for this scenario where minimal effort is desired.
- B. Incorrect.
AWS DeepRacer is a service for learning reinforcement learning through autonomous driving simulations. It is not related to building recommendation systems.
- C. Correct.
Amazon Personalize is a fully managed service that enables developers with limited ML expertise to create personalized recommendations for users. It is specifically designed for use cases like product recommendations and fits the requirements of this scenario.
- D. Incorrect.
Amazon Rekognition is a service for analyzing images and videos, such as detecting objects, faces, or text. It is not relevant to building a recommendation system.