AI Systems · Systems and Thinking
Model Training and Inference
Training can develop or adjust a model. Inference can use a model to produce an output. These are different roles. A model can be one part of an AI system. An output is not automatically correct.
Sources & review
Evidence and review
How Atlas supports this record.
Atlas uses original Kinesema wording. These public sources support narrow parts of the record; they are not endorsements or proof of claims outside the roles shown below.
- Review
- Human reviewed
- AI assistance
- Yes—human reviewed
- Last reviewed
- 2026-08-16
Explanatory memorandum on the updated OECD definition of an AI system
Organisation for Economic Co-operation and Development
- Used here for
- The scoped distinction between an AI system and a model component; machine-learning and knowledge-based technique families; and the roles of training in model development and inference in producing outputs.
- Source use
- Paraphrased from source
- Source type
- official intergovernmental explanatory material; published 2024
Artificial Intelligence Risk Management Framework (AI RMF 1.0)
National Institute of Standards and Technology
- Used here for
- Wider AI-system and deployment context, lifecycle evaluation, human and organizational roles, and the need to test and monitor system behavior and interpret outputs in context.
- Source use
- Paraphrased from source
- Source type
- official government risk-management framework; version 1.0; published 2023
This is a concise first-release record, not a complete course. Examples, terminology, and reviewed connections can grow through later public projections.
Follow the structure
Connected concepts
These are reviewed Atlas relationships, not suggestions inferred from page order or visual proximity. Open a boundary only when you want the extra detail.
Connected from
Computing and AI Systems
Models, training, and inference can operate as components within a wider AI system built on computing infrastructure.
Connection boundary
This relationship does not make a model equivalent to the complete AI system or imply one universal model lifecycle.
Connected from
Data, Datasets, and Algorithms
Training can use data, an objective, and a learning procedure to develop or adjust a model. Inference then uses a model and input in a different role.
Connection boundary
This relationship does not assess whether a dataset is suitable, representative, lawful, private, or sufficient for a particular model.
Connected from
Deterministic and Probabilistic Systems
Model training and inference may use probabilistic methods while operating inside deterministic software contracts, data pipelines, validation rules, and presentation boundaries.
Connection boundary
Deterministic surrounding code does not make a model output automatically correct, and probabilistic model behavior does not describe the entire system.
Connects to
Retrieval and Evaluation
Training, inference, retrieval, and evaluation are different processes that can interact inside an AI-assisted system. Evaluation may inspect behavior at more than one of those layers.
Connection boundary
Passing a retrieval or model evaluation does not automatically prove answer truth, safety, accessibility, or general capability.