MLS-C01 Question 131
Single answerA retail company wants to recommend products to its customers on their e-commerce platform, based on their past purchase history and browsing behavior. The team has limited technical expertise and wants a solution that can be implemented quickly with minimal effort. Which of the following approaches would be the most appropriate in this situation?
- A
Build a custom machine learning model to predict product recommendations using customer data.
- B
Use AWS Personalize to generate product recommendations based on customer interaction data.
- C
Implement a simple rule-based system that recommends the most popular products.
- D
Hire a data science team to design and train a recommendation engine from scratch.
Show answer and explanation
Correct answer: B
Explanation
AWS Personalize is a managed machine learning service designed to provide fast and accurate personalized recommendations without requiring deep ML expertise. It is ideal in this scenario as it aligns with the company's requirements for a quick and low-effort solution while still leveraging machine learning effectively. The other options either require more effort, expertise, or do not meet the personalization requirement.
- A. Incorrect.
Building a custom machine learning model can be time-consuming and requires significant expertise. This approach is not ideal given the team's limited technical expertise and need for a quick solution.
- B. Correct.
AWS Personalize is a managed service that simplifies the process of building recommendation systems. It is specifically designed for use cases like this and requires minimal machine learning expertise, making it the most appropriate choice.
- C. Incorrect.
A rule-based system may be quick to implement but will not provide personalized recommendations based on browsing and purchase history, which is a key requirement in this scenario.
- D. Incorrect.
Hiring a data science team is a lengthy and expensive process, which does not align with the company's need to implement a solution quickly with minimal effort.