This is often caused by uncontrolled randomness in the pipeline. Random seeds affect data splits, model initialization, and even parallel execution order. If seeds aren’t fixed consistently, results will vary.

Set seeds for all relevant libraries and document them as part of the experiment. Also check whether data ordering or sampling changes between runs. In distributed environments, nondeterminism can still occur due to hardware or parallelism, so expect small variations.

Common mistakes include: Setting a seed in only one library, Assuming deterministic behavior by default and Comparing runs across different environments

The takeaway is that reproducibility requires intentional control, not assumptions.