Training
Eğitim
D1
Training is the process of improving a system’s performance with machine-learning techniques, commonly by fitting learnable model parameters using training data.
Review status: 2027-02-23
Technical explanation
In a machine-learning workflow, training produces a model from training data and can improve a system’s performance.
Conceptual boundaries
Training is the learning process, whereas inference uses a model to generate outputs; it is not the same as merely collecting data or putting a model into use.
Provider-neutral example
A team can fit a model’s learnable parameters on historical demand data before using it to propose next-week quantities.
Limitations
This entry concerns machine-learning training and does not prescribe a particular training method.
Related concepts
Atomic claims and evidence
1.1OECD, Explanatory Memorandum on the Updated OECD Definition of an AI System
- Source
- OECD, Explanatory Memorandum on the Updated OECD Definition of an AI System
- Source role
- Authoritative source
- Exact locator
- Building AI systems and models, machine-learning approaches, p. 7
- Supported claim
- OECD describes training as the process of improving a system’s performance using machine-learning techniques.
- Last verification
- Review due
- Scope limitation
- This description concerns machine-learning training and does not cover every kind of software development.
2.1Regulation (EU) 2024/1689 (Artificial Intelligence Act)
- Source
- Regulation (EU) 2024/1689 (Artificial Intelligence Act)
- Source role
- Authoritative source
- Exact locator
- Article 3(29)
- Supported claim
- The EU AI Act defines training data as data used to train an AI system through fitting its learnable parameters.
- Last verification
- Review due
- Scope limitation
- This is the Act’s terminology and does not prescribe a particular training method.