AI engineering glossary · ML fundamentals
What is reproducibility?
Reproducibility: The ability to obtain the same results again from the same data, code and settings. It requires recording versions, seeds and configurations, and it is essential for trusting experiments and debugging.
Reproducibility: an example
Logging the dataset version, code commit and random seed lets a colleague rerun your experiment and get the same 0.87 score.
Related terms
More ML fundamentals terms
- Selection bias
- Self-supervised learning
- Semi-supervised learning
- Shortcut learning
- Spurious correlation
- Stratified k-fold
- Structural risk minimisation
- Supervised learning
Where you learn it
Reproducibility is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.