What does reproducibility mean in the MLOps context?
Reproducibility in the MLOps context means that an AI training run can be re-executed at any time under the same conditions and produce the same result. To achieve this, training data, code versions, model parameters, and configurations are recorded unambiguously.
This makes it possible to trace how a model was created, why it achieved a particular performance level, and which changes had which effects. Reproducibility creates technical transparency and is the foundation for quality assurance, governance, and regulatory requirements.
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