Human-in-the-Loop (HITL) is an iterative feedback process crucial to machine learning and computer vision projects, where human input significantly influences model development. By integrating human feedback, models, particularly in fields like computer vision, can achieve higher accuracy and faster learning rates, as they receive guidance akin to a parent teaching a child the difference between a cat and a dog. HITL processes are applicable in both supervised and unsupervised learning, with human annotators providing labeled datasets or aiding in the labeling of largely unlabeled data, enhancing model accuracy and output quality. While HITL can slow down processes due to potential human errors and the slower pace of human work compared to algorithms, it proves invaluable in fields such as healthcare, quality assurance in manufacturing, and when working with rare datasets. Encord offers a platform that supports HITL processes, facilitating data annotation, model diagnostics, and active learning, thereby improving machine learning outcomes and accelerating model development across various sectors.