Reproducibility
Definition
Reproducibility makes results comprehensible again based on documented foundations.
The possibility to reproduce or verifiably confirm a result with documented data, methods, code, and environment.
Simply explained
With the same recorded ingredients and steps, a result should be restorable or confirmable.
Why it matters in practice
The term helps to select, develop, evaluate AI systems in a well-founded manner and realistically assess their technical risks.
Example from business
A model training can be rerun with versioned dataset, seed, code, and dependencies.
Limits and pitfalls
- Confusing the same seed with full reproducibility or not recording hardware and data versions.
Origin & history
Reproducibility is a fundamental principle of science and has been concretized for data- and software-based research.
Use in mid-sized companies
- Possible example: A model training can be rerun with versioned dataset, seed, code, and dependencies.
Frequently asked questions
What does reproducibility mean?
The possibility to reproduce or verifiably confirm a result with documented data, methods, code, and environment. What mistakes should be avoided? Confusing the same seed with full reproducibility or not recording hardware and data versions.
Sources
Source checked on 10.10.2026. Editorial application examples are hypothetical; no evidence of real customer results.
Editorially reviewed on: