{"id":464833,"date":"2024-10-20T10:37:11","date_gmt":"2024-10-20T10:37:11","guid":{"rendered":"https:\/\/pdfstandards.shop\/product\/uncategorized\/bs-iso-iec-5259-12024\/"},"modified":"2024-10-26T19:34:53","modified_gmt":"2024-10-26T19:34:53","slug":"bs-iso-iec-5259-12024","status":"publish","type":"product","link":"https:\/\/pdfstandards.shop\/product\/publishers\/bsi\/bs-iso-iec-5259-12024\/","title":{"rendered":"BS ISO\/IEC 5259-1:2024"},"content":{"rendered":"
PDF Pages<\/th>\n | PDF Title<\/th>\n<\/tr>\n | ||||||
---|---|---|---|---|---|---|---|
2<\/td>\n | undefined <\/td>\n<\/tr>\n | ||||||
6<\/td>\n | Foreword <\/td>\n<\/tr>\n | ||||||
7<\/td>\n | Introduction <\/td>\n<\/tr>\n | ||||||
9<\/td>\n | 1 Scope 2 Normative references 3 Terms and definitions <\/td>\n<\/tr>\n | ||||||
13<\/td>\n | 4 Symbols and abbreviated terms 5 Data quality concepts for analytics and machine learning 5.1 Data quality considerations for analytics and machine learning 5.1.1 General 5.1.2 Machine learning and data quality <\/td>\n<\/tr>\n | ||||||
14<\/td>\n | 5.1.3 Data characteristics that pose quality challenges for analytics and machine learning 5.1.4 Data sharing, data re-use and data quality for analytics and machine learning 5.2 Data quality concept framework for analytics and machine learning 5.2.1 Overview <\/td>\n<\/tr>\n | ||||||
15<\/td>\n | 5.2.2 Data quality management <\/td>\n<\/tr>\n | ||||||
18<\/td>\n | 5.2.3 Data quality governance 5.2.4 Data provenance 5.3 Data life cycle for analytics and ML 5.3.1 Overview 5.3.2 Data life cycle model <\/td>\n<\/tr>\n | ||||||
21<\/td>\n | 5.3.3 Processes across the multiple stages <\/td>\n<\/tr>\n | ||||||
23<\/td>\n | Annex A (informative) Examples and scenarios <\/td>\n<\/tr>\n | ||||||
26<\/td>\n | Bibliography <\/td>\n<\/tr>\n<\/table>\n","protected":false},"excerpt":{"rendered":" Artificial intelligence. Data quality for analytics and machine learning (ML) – Overview, terminology, and examples<\/b><\/p>\n |