ISO/IEC TR 5469:2024
PD ISO/IEC TR 5469:2024
International
ISO/IEC AWI TS 5471
Artificial intelligence — Quality evaluation guidelines for AI systems
This document provides guidelines for evaluation of AI systems using an AI system quality model. The document is applicable to…
ISO/IEC 24029-2:2023
Artificial intelligence (AI) — Assessment of the robustness of neural networks — Part 2: Methodology for the use of formal methods
ISO/IEC TR 24029-1:2021
Artificial Intelligence (AI) – Assessment of the robustness of neural networks – Part 1: Overview
This document provides background about existing methods to assess the robustness of neural networks. © ISO/IEC 2022 All rights reserved
ISO/IEC FDIS 5259-2
ISO/IEC NP 5259-2
Artificial intelligence — Data quality for analytics and machine learning (ML) — Part 2: Data quality measures
This document provides a data quality model, data quality measures, and guidance on reporting data quality in the context of…
ISO/IEC FDIS 5259-3
ISO/IEC NP 5259-3
Artificial intelligence — Data quality for analytics and machine learning (ML) — Part 3: Data quality management requirements and guidelines
This document specifies requirements and provides guidance for establishing, implementing, maintaining and continually improving the quality for data used in…
ISO/IEC FDIS 5259-4
ISO/IEC 5259-4
Artificial intelligence — Data quality for analytics and machine learning (ML) — Part 4: Data quality process framework
ISO/IEC DIS 5259-1
Artificial intelligence — Data quality for analytics and machine learning (ML) — Part 1: Overview, terminology, and examples
This document provides the landscape for understanding and associating of “Data quality for analytics and ML” series and guides the…
This Recommendation describes the architecture, functional entities, and interfaces for a spontaneous dialogue processing system for language learning. © ITU…
ITU-T Y.3179
Architectural framework for machine learning model serving in future networks including IMT-2020
This Recommendation provides an architectural framework for machine learning (ML) models serving in future networks including IMT-2020, i.e., preparing and…
Recommendation ITU-T Y.3172 specifies an architectural framework for machine learning (ML) in future networks including IMT-2020. A set of architectural…
This Recommendation specifies an architectural framework for network automation based on artificial intelligence (AI) for resource and fault management in…
