reproducibility

The same model can score ten points apart on MMLU depending on prompt format and scoring method. The signal is knowing the knobs (log-prob vs generation, few-shot, normalization) that make evals reproducible.

Modern table formats let you query a table as of a past version or timestamp, the backbone of reproducibility, audits, and recovery. The signal is the versioned-snapshot mechanism and where it breaks.

At thousand-GPU scale, hardware failure is the norm, not the exception, and a run that cannot resume wastes weeks. The signal is checkpointing strategy, deterministic resume, and minimizing lost work. Here is the fault-tolerant design.

Reproducibility is what separates an ML platform from a pile of notebooks. The signal is tracking the full provenance (data, code, config, metrics) and a registry that governs promotion. Here is what to track and why each piece matters.

When a model regresses or an auditor asks 'how was this built,' guesswork is a failing answer. The signal is tracking the whole chain so any model is reproducible and any past decision is explainable. Here is the chain.

'Why was I denied?' is a question regulators and customers will ask, and 'we retrained since then' is not an acceptable answer. Exact reproduction means versioning everything. Here is what 'everything' actually means.

'Works on my machine' is a dependency and environment problem, and for ML it includes CUDA versions and exact library pins that change numerical results. Here is how to make the production environment match training.

'Just set a seed' is the wrong answer. Reproducibility means pinning the environment, the data, the code, and the hardware-level nondeterminism, all at once. Here is the full checklist and where it leaks.

You cannot put a 2 TB dataset in git, and copying it per experiment bankrupts you. The signal is knowing how content-addressed versioning works and when file-level (DVC) versus branch-level (lakeFS) fits. Here is the decision.