Exploring human-in-the-loop labeling tools

Human-in-the-loop labeling frameworks are critical for enabling effective collaboration between humans and ML systems. In this section, we will explore some of the leading human-in-the-loop labeling tools for active ML.

We will look at how these frameworks allow humans to provide annotations, verify predictions, adjust model confidence thresholds, and guide model training through interfaces and workflows optimized for human-AI collaboration. Key capabilities provided by human-in-the-loop frameworks include annotation-assisted active ML, human verification of predictions, confidence calibration, and model interpretability.

The labeling tools we will examine include Snorkel AI, Prodigy, Encord, Roboflow, and others. We will walk through examples of how these tools can be leveraged to build applied active learning systems with effective human guidance. The strengths and weaknesses of different approaches will be discussed. By the end of...