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…

Leveraging Amazon SageMaker Ground Truth's automated data labeling feature, also known as active learning, significantly reduces the manual effort required from the internal team. This feature uses a machine learning model to automatically label a large portion of the data, routing only uncertain samples to the human workforce for review, which is ideal for a small team with no ML expertise, and the SageMaker Object Detection algorithm is appropriate for identifying solar panels in images.

Requiring the private internal workforce to manually label all 8,000 images would demand the maximum amount of effort from the team, directly contradicting the requirement for the "LEAST amount of effort."

While custom labeling workflows offer flexibility, setting them up and managing them requires more expertise and effort than utilizing Ground Truth's built-in automated data labeling feature, especially for a team with no ML experience.

Using a public workforce might reduce direct internal labeling effort but introduces other complexities like quality control and worker management, and it still lacks the efficiency benefit of the automated data labeling feature, which is the key to minimizing effort for the labeling task itself.