Data Labeling: The Authoritative Guide

This post serves as an authoritative guide to data labeling for machine learning. It defines data labeling, explains its importance in supervised learning, and details various data types (images, video, 3D, audio, text). It outlines annotation strategies, including automated, human-only, and human-in-the-loop (HITL) approaches, and discusses considerations for assembling a labeling workforce (in-house, friends/family, third-party companies). The guide emphasizes the critical role of high-quality annotations for model performance and provides practical advice for achieving them.