vault / mlops / reproducibility.md

Reproducibility

ML platform 71 words 1 outgoing 1 backlinks

Same data, same code, same result - or at least a record of why not. The minimum kit: pinned dependencies, versioned data or a snapshot reference, seeded randomness, and the run’s config stored next to its output. “Worked on rerun” dies by a thousand unpinned cuts: a floating base-image tag, a silent data refresh, an upstream default that changed. If you cannot diff two runs, you cannot trust the second one.

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[[ Kubeflow Pipelines ]] Kubeflow Pipelines turns train-test-deploy into a DAG of containerized components on Kubernetes, each with explicit inputs and outputs. It earns its weight when pipelines are long, rerunnable and shared; a two-step nightly job is happier as a script. Gotchas: a...