What is the purpose of Amazon SageMaker Ground Truth?

Amazon SageMaker Ground Truth is designed specifically to create high-quality labeled training datasets by leveraging human labeling, which makes it the most suitable tool for this purpose. High-quality labeled data is essential for training machine learning models effectively, as the accuracy of these models largely relies on the quality of the input data.

Ground Truth provides an efficient way to manage data labeling for machine learning projects by integrating human workers (or labeling services) with automation. It supports various labeling workflows that can involve human annotators, making it capable of handling different types of data—like images, text, and videos—across various use cases.

The other options, while they describe important features or components of machine learning processes, do not align with the core function of Ground Truth. Visualizing machine learning workflows, automating model deployment, and monitoring real-time data streams are handled by other tools and services within the AWS ecosystem. These functionalities are not the focus of SageMaker Ground Truth, which is dedicated to the critical step of ensuring that training datasets are accurately labeled for successful model training.