# ML Reproducibility ML reproducibility is the property that re-running the same code on the same data with the same seeds produces the same result — or close enough that conclusions hold. Hard in practice because ML pipelines depend on data versions, library versions, hardware non-determinism (GPU kernels), random seeds, and the exact order of operations. The practical bar: log everything needed to recreate the run — code commit, data snapshot, environment lockfile, seeds — even if exact bit-for-bit reproduction isn't possible. ## References ## Related - [[MLflow]] - [[MLOps]]