Starting in the mid-2010s, companies began to emerge with a specific focus on providing data annotation and labeling services. These companies recognized the challenges associated with creating accurate and diverse training datasets for AI models. They combined crowdsourcing, automation, and human expertise to deliver high-quality labeled data for a wide range of applications.
Throughout this timeline, the development of data collection and annotation services was closely tied to the evolution of AI technologies, the increasing complexity of AI models, and the recognition of the critical role that labeled data plays in the success of machine learning projects. As the AI landscape continues to evolve, data collection and annotation services remain essential to training robust and accurate AI models across various domains