Artificial intelligence - Data quality for analytics and machine learning (ML) - Part 4: Data quality process framework
Abstract
This document establishes general common organizational approaches, regardless of the type, size or nature of the applying organization, to ensure data quality for training and evaluation in analytics and machine learning (ML). It includes guidance on the data quality process for: - supervised ML with regard to the labelling of data used for training ML systems, including common organizational approaches for training data labelling; - unsupervised ML; - semi-supervised ML; - reinforcement learning; - analytics. This document is applicable to training and evaluation data that come from different sources, including data acquisition and data composition, data preparation, data labelling, evaluation and data use. This document does not define specific services, platforms or tools.
Begin
2024-10-10
WI
JT021043
Planned document number
DIN EN ISO/IEC 5259-4
Project number
04301164
Responsible national committee
Responsible european committee
CEN/CLC/JTC 21/WG 3 - Engineering aspects
