ML projects typically require thousands or even millions of labeled training items to be successful. While the goals of machine learning projects can vary widely in complexity, they all share a common requirement: a large volume of high-quality data to train the model. Most companies simply don’t have the existing resources to staff for large-scale data annotation projects, and it’s expensive to pull engineers and other team members off of their core work on your product to perform data labeling tasks.
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