Discover how Amazon Rekognition Custom Labels streamlines targeted solar panel marketing by enabling businesses to identify solar-equipped homes using machine learning, even without ML expertise.
Table of Contents
Question
A company wants to conduct targeted marketing to sell solar panels to homeowners. The company wants to use machine learning (ML) technologies to identify which houses already have solar panels. The company has collected 8,000 satellite images as training data and will use Amazon SageMaker Ground Truth to label the data.
The company has a small internal team that is working on the project. The internal team has no ML expertise and no ML experience.
Which solution will meet these requirements with the LEAST amount of effort from the internal team?
A. Set up a private workforce that consists of the internal team. Use the private workforce and the SageMaker Ground Truth active learning feature to label the data. Use Amazon Rekognition Custom Labels for model training and hosting.
B. Set up a private workforce that consists of the internal team. Use the private workforce to label the data. Use Amazon Rekognition Custom Labels for model training and hosting.
C. Set up a private workforce that consists of the internal team. Use the private workforce and the SageMaker Ground Truth active learning feature to label the data. Use the SageMaker Object Detection algorithm to train a model. Use SageMaker batch transform for inference.
D. Set up a public workforce. Use the public workforce to label the data. Use the SageMaker Object Detection algorithm to train a model. Use SageMaker batch transform for inference.
Answer
A. Set up a private workforce that consists of the internal team. Use the private workforce and the SageMaker Ground Truth active learning feature to label the data. Use Amazon Rekognition Custom Labels for model training and hosting.
Explanation
The solution that meets the requirements with the least effort from the internal team is:
A. Set up a private workforce that consists of the internal team. Use the private workforce and the SageMaker Ground Truth active learning feature to label the data. Use Amazon Rekognition Custom Labels for model training and hosting.
Amazon Rekognition Custom Labels simplifies the entire machine learning workflow, from data labeling to model training and hosting, making it ideal for teams without ML experience. SageMaker Ground Truth’s active learning feature intelligently selects the most informative images for labeling, reducing the manual effort required from the private workforce.
By using a private workforce consisting of the internal team, the company maintains control and privacy over the data labeling process. Amazon Rekognition Custom Labels abstracts away the complexities of model training and hosting, allowing the team to focus on the business problem at hand.
This solution minimizes the effort required from the internal team while leveraging the power of machine learning to accurately identify homes with solar panels for targeted marketing campaigns.
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