Reproducibility
Reproducibility in the context of production machine-learning systems.
Lessons
Formal explanations, examples, simulations, and checkpoints.
From notebook to reproducible run
A reproducible result is a result whose complete input identity can be replayed.
Engineering foundationsContracts before models
Contracts keep bad meaning from becoming good-looking features.
Engineering foundationsTraining code is product code
The shipped model is the reloaded artifact, not the object still living in the trainer.
Engineering foundationsContainers and configuration without surprises
A container is reproducible only when its image, inputs, configuration, and outputs all have explicit identities.
Engineering foundationsObservable, idempotent, failure-safe workflows
A safe workflow makes commitment atomic and repetition uneventful.
Engineering foundationsRead a research paper as an executable specification
Turn a research paper into a testable claim map, implementation contract, and prioritized reading plan before writing code.
Engineering foundationsReproduce, validate, and productionize a paper
Implement a paper from baseline to novel component, diagnose reproduction gaps, and adapt it for production without silently changing its claim.
Courses & assignments
Dependency-authoritative learning units.
Casebook
Reported facts and course reconstructions.
The winding road through TFX and Kubeflow
Platform adoption is a product and socio-technical problem.
Netflix · Data & platformMetaflow: a human-centric path from notebook to production
The best abstraction preserves the user’s reasoning model while adding production guarantees.