How to Organize Data Labeling for Machine Learning: Approaches and Tools

If there was a data science hall of fame, it would have a section dedicated to labeling. The labelers' monument could be Atlas holding that large rock symbolizing their arduous, detail-laden responsibilities. ImageNet -- an image database -- would deserve its own stele. Just thinking about it makes you tired. While labeling is not launching a rocket into space, it's still seriously business. Labeling is an indispensable stage of data preprocessing in supervised learning. Historical data with predefined target attributes (values) is used for this model training style. An algorithm can only find target attributes if a human mapped them. Labelers must be extremely attentive because each mistake or inaccuracy negatively affects a dataset's quality and the overall performance of a predictive model. How to get a high-quality labeled dataset without getting grey hair?