Most deep learning applications require lots of labeled data. There are publicly available datasets that can serve as a starting point, but there is no competitive advantage of doing so.
Most companies usually spend a lot of money and time to label their own data.
Data flywheel means harnessing the power of users rapidly improve the whole machine learning system.
Semi-supervised learning is a relatively recent learning technique where the training data is autonomously (or automatically) labeled.
Data augmentation is a strategy that enables practitioners to significantly increase the diversity of data available for training models, without actually collecting new data.
Synthetic data is data that’s generated programmatically, an underrated idea that is almost always worth starting with.