Standard
Track updates
AS ISO/IEC 5259.4:2024
[Current]Artificial intelligence - Data quality for analytics and machine learning (ML), Part 4: Data quality process framework
AS ISO/IEC 5259.4:2024 identically adopts ISO/IEC 5259 4:2024, which 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).
Published: 13/09/2024
Pages: 29
Table of contents
Cited references
Content history
Table of contents
Header
About this publication
Preface
Foreword
Introduction
1 Scope
2 Normative references
3 Terms and definitions
4 Symbols and abbreviated terms
5 Data quality process principles
6 Data quality process framework
6.1 General
6.2 Data quality planning
6.3 Data quality evaluation
6.4 Data quality improvement
6.5 Data quality process validation
6.6 Using the DQPF
7 Data quality process for ML
7.1 General
7.2 Data requirements
7.3 Data planning
7.4 Data acquisition
7.5 Data preparation
7.5.1 General
7.5.2 Supervised ML
7.5.3 Unsupervised ML
7.5.4 Semi-supervised ML
7.5.5 Dataset composition
7.5.6 Data labelling
7.5.7 Data annotation
7.5.8 Data quality assessment
7.5.9 Data quality improvement
7.5.9.1 General
7.5.9.2 Data cleaning
7.5.9.3 Data normalization, standardization and imputation
7.5.9.3.1 Data normalization
7.5.9.3.2 Data standardization
7.5.9.3.3 Data imputation
7.5.9.4 Data augmentation
7.5.10 Data de-identification
7.5.11 Data encoding.
7.6 Data provisioning
7.6.1 General
7.6.2 Supervised ML
7.6.3 Unsupervised ML
7.6.4 Semi-supervised ML
7.7 Data decommissioning
8 Data labelling methods and process
8.1 General
8.2 Data labelling principles
8.3 Data labelling methods
8.4 Data labelling process
8.4.1 General
8.4.2 Labelling specifications
8.4.3 Labelling participant roles
8.4.4 Labelling tools or platforms
8.4.5 Labelling task establishment
8.4.6 Labelling task assignment
8.4.7 Labelling process control
8.4.8 Labelling result quality checking
8.4.9 Labelling result revision
9 Roles of participants
9.1 General
9.2 Data planner
9.3 Data originator
9.4 Data collector
9.5 Data engineer
9.6 Data holder
9.7 Data user
10 Data quality process for semi-supervised ML
10.1 General
10.2 Data requirements
10.3 Data planning
10.4 Data acquisition
10.5 Data preparation
10.6 Data provisioning
10.7 Data decommissioning
11 Data quality process for reinforcement learning
11.1 General
11.2 Data requirements
11.3 Data planning
11.4 Data acquisition
11.5 Data preparation
11.5.1 General process
11.5.2 Data recording
11.6 Data provisioning
11.7 Data decommissioning
12 Data quality process for analytics
12.1 General
12.2 Data requirements
12.3 Data planning
12.4 Data acquisition
12.4.1 General
12.4.2 Data loading
12.4.3 Data storage
12.5 Data preparation
12.5.1 General
12.5.2 Data cleaning
12.5.3 Data transformation
12.5.4 Data aggregation
12.5.5 Data quality assessment
12.5.6 Data quality improvement
12.6 Data provisioning
12.7 Data decommissioning
Bibliography
Cited references in this standard