Question 24 - MLS-C01 discussion

The solution A will meet the requirements with the least amount of effort from the internal team because it uses Amazon SageMaker Ground Truth and Amazon Rekognition Custom Labels, which are fully managed services that can provide the desired functionality. The solution A involves the following steps:

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. Amazon SageMaker Ground Truth is a service that can create high-quality training datasets for machine learning by using human labelers. A private workforce is a group of labelers that the company can manage and control. The internal team can use the private workforce to label the satellite images as having solar panels or not.The SageMaker Ground Truth active learning feature can reduce the labeling effort by using a machine learning model to automatically label the easy examples and only send the difficult ones to the human labelers1.

Use Amazon Rekognition Custom Labels for model training and hosting. Amazon Rekognition Custom Labels is a service that can train and deploy custom machine learning models for image analysis. Amazon Rekognition Custom Labels can use the labeled data from SageMaker Ground Truth to train a model that can detect solar panels in satellite images.Amazon Rekognition Custom Labels can also host the model and provide an API endpoint for inference2.

The other options are not suitable because:

Option B: Setting up a private workforce that consists of the internal team, using the private workforce to label the data, and using Amazon Rekognition Custom Labels for model training and hosting will incur more effort from the internal team than using SageMaker Ground Truth active learning feature.The internal team will have to label all the images manually, without the assistance of the machine learning model that can automate some of the labeling tasks1.

Option C: Setting up a private workforce that consists of the internal team, using the private workforce and the SageMaker Ground Truth active learning feature to label the data, using the SageMaker Object Detection algorithm to train a model, and using SageMaker batch transform for inference will incur more operational overhead than using Amazon Rekognition Custom Labels. The company will have to manage the SageMaker training job, the model artifact, and the batch transform job.Moreover, SageMaker batch transform is not suitable for real-time inference, as it processes the data in batches and stores the results in Amazon S33.

Option D: Setting up a public workforce, using the public workforce to label the data, using the SageMaker Object Detection algorithm to train a model, and using SageMaker batch transform for inference will incur more operational overhead and cost than using a private workforce and Amazon Rekognition Custom Labels. A public workforce is a group of labelers from Amazon Mechanical Turk, a crowdsourcing marketplace. The company will have to pay the public workforce for each labeling task, and it may not have full control over the quality and security of the labeled data.The company will also have to manage the SageMaker training job, the model artifact, and the batch transform job, as explained in option C4.

References:

1: Amazon SageMaker Ground Truth

2: Amazon Rekognition Custom Labels

3: Amazon SageMaker Object Detection

4: Amazon Mechanical Turk