Effectively managing human-in-the-loop systems

Getting high-quality annotations requires finding, vetting, supporting, and retaining effective labelers. It is crucial to build an appropriate labeling team that meets the requirements of the ML project.

The first option is to establish an internal labeling team. This involves hiring full-time employees to label data, which enables close management and training. Cultivating domain expertise is easier when done internally. However, there are drawbacks to this, such as higher costs and turnover. This option is only suitable for large, ongoing labeling requirements.

Another option is to crowdsource labeling tasks using platforms such as ScaleAI, which allow labeling tasks to be distributed to a large, on-demand workforce. This option provides flexibility and lower costs, but it can lack domain expertise. Quality control becomes challenging when working with anonymous crowd workers.

You could use third-party labeling services, such...