Data Annotation

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.

#Annotation