ISO/IEC 23053
An international standard for describing how machine learning systems are built and work, a common blueprint language for explaining ML architecture to diverse stakeholders.
The Simple Version
An international standard for describing how machine learning systems are built and work, a common blueprint language for explaining ML architecture to diverse stakeholders.
Detailed Explanation
ISO/IEC 23053:2022 (Framework for Artificial Intelligence (AI) Systems Using Machine Learning (ML)) defines a conceptual ML system framework covering components such as data collection, data pre-processing, model training, model evaluation, deployment, and monitoring, together with the interfaces and data flows between them. It provides a reference architecture that supports interoperability between AI standards and enables consistent documentation of ML systems for governance and audit purposes. The framework underpins more specific standards including the ISO/IEC 5259 data quality series.
Key Characteristics
- Provides a reference architecture for ML system components and data flows
- Enables consistent documentation across diverse ML system types
- Underpins data quality standards including ISO/IEC 5259
- Developed by ISO/IEC JTC 1/SC 42
Why It Matters
Technical documentation teams can use ISO/IEC 23053 as a structural framework for Annex IV documentation, mapping their specific ML architecture to the standard's reference components to ensure completeness.
Real-World Analogy
Like a standard reference architecture for building construction, specifying how load-bearing walls, plumbing, electrical systems, and insulation relate to each other, without prescribing specific materials or designs.
Common Misconceptions
- ISO/IEC 23053 prescribes a specific ML architecture to implement, it provides a description framework, not a prescriptive architecture.
- The standard covers deep learning and transformer models only, it covers all ML paradigms and can be applied to classical ML, deep learning, and hybrid systems.