Ground truth in machine learning: Definition & best practices

This post details the definition and best practices for ground truth in machine learning. It covers types of ground truth (human annotation, expert annotation, behavioral inference, synthetic ground truth), its role across the ML lifecycle (training, validation, testing, production monitoring), and common challenges like label leakage, annotation drift, feedback loops, and distribution mismatch. It also specifically addresses the ground truth problem for AI analytics, emphasizing the need for context and governed semantic models. Best practices include treating ground truth as infrastructure, investing in data quality monitoring, and establishing clear processes for data collection, labeling, and validation.