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Computer Vision on AWS
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Labeled data is key to developing an accurate and effective model using a supervised machine learning algorithm. Typically, machine learning practitioners spend 70% of their time labeling and managing data. It slows down innovation and increases cost. We saw in Chapter 3, and Chapter 7, how we needed high-quality labeled data to develop custom ML models. Although those services allowed a labeling interface in the built-in console, if you have a large number of images in your dataset, it can quickly become a monumental and cumbersome task to label them. You would either need to outsource the labeling responsibility or would need a solution to split the labeling workload across multiple labelers. Building such a solution is undifferentiated heavy lifting for ML practioners who would just want to focus on developing accurate models.
The solution to this labeling challenge is Amazon SageMaker Ground Truth (GT). As the name of the service...