Sagemaker

Question 1

SageMaker JumpStart

Answer 1

SageMaker JumpStart is the correct choice for the e-commerce startup as it provides access to pre-built machine learning models and workflows, allowing them to quickly deploy a high-quality recommendation system without the need for extensive machine learning expertise. This feature helps them save time and resources by leveraging ready-to-use models tailored for specific use cases.

Question 2

SageMaker Ground Truth

Answer 2

SageMaker Ground Truth is not the most suitable choice for the e-commerce startup in this scenario. Ground Truth is primarily used for labeling datasets to train machine learning models, which may not align with the startup’s goal of quickly deploying a recommendation system with limited resources and expertise in machine learning.

Question 3

SageMaker Studio

Answer 3

SageMaker Studio is a fully integrated development environment for machine learning, providing tools for data preparation, model training, and deployment. While it offers a comprehensive set of features for machine learning projects, it may be too complex and resource-intensive for the e-commerce startup’s goal of deploying a recommendation system quickly and cost-effectively. SageMaker JumpStart would be a more suitable choice in this scenario.

Question 4

SageMaker Model Monitor

Answer 4

Model Monitor is used for monitoring the performance and quality of deployed machine learning models, ensuring they meet predefined criteria for accuracy and fairness. While important for model maintenance, it does not directly address the startup’s need to deploy a high-quality model quickly and at a low cost.

Question 5

Amazon SageMaker Autopilot

Answer 5

Amazon SageMaker Autopilot is designed for automating the process of finding the best hyperparameters.