Detail of GB/T 43782-2024

Introduction of GB/T 43782-2024

Artificial intelligence - Technical requirements for machine learning system 1 Scope This document presents a machine learning system framework, and specifies the functionality, reliability, maintainability, compatibility, security, and extensibility requirements. This document is applicable to the basis for planning, research and development, evaluation, selection and acceptance of machine learning support service systems and related solutions in various fields. 2 Normative references The following documents contain provisions which, through reference in this text, constitute provisions of this document. For dated references, only the edition cited applies. For undated references, the latest edition of the referenced document (including any amendments) applies. GB/T 17235.1 Information technology - Digital compression and coding of continuous-tone still images - Part 1: Requirements and guidelines GB/T 33475.2 Information technology - High efficiency media coding - Part 2: Video GB/T 33475.3 Information technology - High efficiency media coding - Part 3: Audio GB/T 41867-2022 Information technology - Artificial intelligence - Terminology GB/T 42018-2022 Information technology - Artificial intelligence - Platform computing resource specification ISO/IEC 14496-10 Information technology - Coding of audio- visual objects - Part 10: Advanced video coding ISO/IEC 15948 Information technology - Computer graphics and image processing - Portable Network Graphics (PNG): Functional specification ISO/IEC 23008-2 Information technology - High efficiency coding and media delivery in heterogeneous environments - Part 2: High efficiency video coding ISO/IEC 23008-3 Information technology - High efficiency coding and media delivery in heterogeneous environments - Part 3: 3D audio 3 Terms and definitions For the purposes of this document, the terms and definitions given in GB/T 41867-2022, GB/T 42018-2022 and the following apply. 3.1 machine learning system software system capable of running or developing machine learning models, algorithms and related application 3.2 machine learning framework software library that uses the pre-built and optimized component set to define model and realize the encapsulation of machine learning algorithms, data call processing and usage of computing resources 3.3 machine learning service IT services provided for organizations or individuals with the value in a convenient way they desire by using machine learning models, algorithms and their systems as tools Note: Machine learning algorithm service is a type of machine learning service, which is used to accept user application requests, select rows for input data processing, and return processing results. 4 Abbreviations For the purposes of this document, the following abbreviations apply. ASIC: Application-Specific Integrated Circuit CPU: Central Processing Unit DAG: Directed Acyclic Graph FPGA: Field Programmable Gate Array GPU: Graphic Processing Unit IDE: Integrated Development Environment JSON: JavaScript Object Notation REST: Representational State Transfer RPC: Remote Procedure Call SOA: Service-Oriented Architecture SQL: Structured Query Language XML: Extensible Markup Language

Contents of GB/T 43782-2024

Contents Foreword i 1 Scope 2 Normative references 3 Terms and definitions 4 Abbreviations 5 System framework 5.1 General 5.2 Machine learning runtime components 5.3 Machine learning framework 5.4 Machine learning service components 5.5 Tools 5.6 Operation and maintenance management 6 Functional requirements 6.1 Machine learning runtime components 6.2 Machine learning framework 6.3 Machine learning service components 6.4 Tools 6.5 Operation and maintenance management 7 Reliability requirements 8 Maintainability requirements 9 Compatibility requirements 9.1 Software compatibility requirements 9.2 Hardware compatibility requirements 10 Security requirements 11 Extensibility requirements Bibliography