datamerkkauksina
Datamerkkauksina refers to the practice of annotating data for machine learning and data-driven analysis. It involves labeling data examples to create supervised learning datasets, where humans or automated processes assign labels, tags, or structured metadata to raw data. The aim is to enable algorithms to learn the relationships between inputs and outputs.
The practice spans several modalities. In images and videos, annotation may include object labels, bounding boxes,
Annotation workflows typically combine tooling with human judgment. Methods range from full manual labeling by trained
Datamerkkauksina underpins many AI applications, including computer vision, natural language processing, speech interfaces, and medical imaging.
Ethical and practical considerations include mitigating label bias, protecting sensitive information, and ensuring appropriate consent for