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.
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.
COBAZ is the simple and effective solution to meet the normative needs related to your activity, in France and abroad.
Available by subscription, CObaz is THE modular solution to compose according to your needs today and tomorrow. Quickly discover CObaz!
Request your free, no-obligation live demo
I discover COBAZ