Volume 1: AI Data Quality Management Guide v3.5
 |
Description
Book Introduction
[Volume 1] AI Data Quality Management Guide
- When building data for artificial intelligence learning, from the construction plan establishment stage, data acquisition/collection, purification, processing, etc.
Providing standards and procedures for procedures, outputs, and quality control activities.
- Establish data construction standards and plans by presenting examples of quality self-inspection processes, quality management standards, and indicators.
Structured to be used as a reference for poetry
[Volume 2] AI Data Building Guide
- Data by data characteristics (LLM, LMM, synthetic) for the 2024 super-large AI data diffusion ecosystem creation project
Includes examples of the data construction process in the acquisition/collection, refinement, processing, and learning stages.
- Includes 1-cycle self-inspection results by data characteristics and provides analysis results on errors that occur.
- Classification of major error types and inclusion of specific examples based on quality verification results
[Volume 3] Generative AI Data Quality Management Guide
- Created to reflect data characteristics (LLM, LMM, synthetic) as the construction of generative AI data expands.
However, for common matters and definitions, refer to the ‘Volume 1 AI Data Quality Management Guide’.
- Provides a reference document that presents characteristics, quality control indicators, and setting guides by learning type so that it can be used as basic data for building high-quality generative AI data.
- When building data for artificial intelligence learning, from the construction plan establishment stage, data acquisition/collection, purification, processing, etc.
Providing standards and procedures for procedures, outputs, and quality control activities.
- Establish data construction standards and plans by presenting examples of quality self-inspection processes, quality management standards, and indicators.
Structured to be used as a reference for poetry
[Volume 2] AI Data Building Guide
- Data by data characteristics (LLM, LMM, synthetic) for the 2024 super-large AI data diffusion ecosystem creation project
Includes examples of the data construction process in the acquisition/collection, refinement, processing, and learning stages.
- Includes 1-cycle self-inspection results by data characteristics and provides analysis results on errors that occur.
- Classification of major error types and inclusion of specific examples based on quality verification results
[Volume 3] Generative AI Data Quality Management Guide
- Created to reflect data characteristics (LLM, LMM, synthetic) as the construction of generative AI data expands.
However, for common matters and definitions, refer to the ‘Volume 1 AI Data Quality Management Guide’.
- Provides a reference document that presents characteristics, quality control indicators, and setting guides by learning type so that it can be used as basic data for building high-quality generative AI data.
index
Summary of AI Data Quality Management Guidelines v3.5
Ⅰ.
outline
Chapter 1 Background and Purpose of Promotion
1.
Background
2.
purpose
Chapter 2: Composition of Quality Management Guidelines
1.
Volume 1: AI Data Quality Management Guide
Chapter 3: Understanding Data for AI Learning
1.
Data for artificial intelligence learning
2.
Data characteristics for artificial intelligence learning
3.
Data Lifecycle for AI Learning
Chapter 4: Understanding Data Quality Management for AI Learning
1.
Data Quality Management Principles for AI Learning
2.
Process of building data for artificial intelligence learning
3.
Scope of data quality management for artificial intelligence learning
4.
Data quality inspection activities for artificial intelligence learning
Ⅱ.
Quality Management System
Chapter 1 Quality Management Framework
1.
Establishing a quality management framework
2.
Defining Quality Management Stakeholders
3.
Quality Management Organization System
Chapter 2 Quality Management Process and Outputs
1.
Preparation and Planning Stage (100)
2.
Construction phase (200)
3.
Operation and utilization stage (300)
4.
Step-by-step deliverables
Chapter 3 Quality Self-Inspection and Quality Verification
1.
outline
2.
Quality self-inspection
3.
Third-party quality verification
Ⅲ.
supplement
Appendix 1.
Quality control standards
1.
Quality control indicators
2.
Construction process adequacy quality control indicators
3.
Data suitability quality control indicators
4.
Processing data accuracy quality control indicators
5.
Learning model suitability quality control indicators
Appendix 2.
Quality Indicator Setting Guide
1.
Quality Indicator Setting Overview
2.
Learning data types
3.
Quality Indicator Setting Guide
Appendix 3.
Data Labeling Guide
Appendix 4.
Privacy Policy Guide
1.
Privacy Policy
2.
Personal Information Protection Guidelines by Life Cycle
Ⅳ.
References
Chapter 1 Definition of Terms
Chapter 2 References
Ⅰ.
outline
Chapter 1 Background and Purpose of Promotion
1.
Background
2.
purpose
Chapter 2: Composition of Quality Management Guidelines
1.
Volume 1: AI Data Quality Management Guide
Chapter 3: Understanding Data for AI Learning
1.
Data for artificial intelligence learning
2.
Data characteristics for artificial intelligence learning
3.
Data Lifecycle for AI Learning
Chapter 4: Understanding Data Quality Management for AI Learning
1.
Data Quality Management Principles for AI Learning
2.
Process of building data for artificial intelligence learning
3.
Scope of data quality management for artificial intelligence learning
4.
Data quality inspection activities for artificial intelligence learning
Ⅱ.
Quality Management System
Chapter 1 Quality Management Framework
1.
Establishing a quality management framework
2.
Defining Quality Management Stakeholders
3.
Quality Management Organization System
Chapter 2 Quality Management Process and Outputs
1.
Preparation and Planning Stage (100)
2.
Construction phase (200)
3.
Operation and utilization stage (300)
4.
Step-by-step deliverables
Chapter 3 Quality Self-Inspection and Quality Verification
1.
outline
2.
Quality self-inspection
3.
Third-party quality verification
Ⅲ.
supplement
Appendix 1.
Quality control standards
1.
Quality control indicators
2.
Construction process adequacy quality control indicators
3.
Data suitability quality control indicators
4.
Processing data accuracy quality control indicators
5.
Learning model suitability quality control indicators
Appendix 2.
Quality Indicator Setting Guide
1.
Quality Indicator Setting Overview
2.
Learning data types
3.
Quality Indicator Setting Guide
Appendix 3.
Data Labeling Guide
Appendix 4.
Privacy Policy Guide
1.
Privacy Policy
2.
Personal Information Protection Guidelines by Life Cycle
Ⅳ.
References
Chapter 1 Definition of Terms
Chapter 2 References
GOODS SPECIFICS
- Date of issue: September 13, 2025
- Page count, weight, size: 294 pages | 188*257*20mm
- ISBN13: 9791129061331
- ISBN10: 1129061337
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