MLOps / data-model-versioning

Data and model versioning

Code, datasets, features, offline/online consistency, training-serving skew, schema validation, and data contracts.

shellbackend needed later
Focus
  • dataset versions
  • feature stores
  • schema validation
  • training-serving skew
Future feature idea

Version graph for code, data, features, schemas, training-serving skew, and contracts.

Self-check
  1. What is the core job of "Data and model versioning"?
  2. Which common mistake would break a production implementation of this topic?
  3. Which inputs or limits must be validated before the interactive feature ships?
  4. What is the smallest test that proves the future implementation behaves correctly?
  5. When does this module really need backend compute, and when is a UI simulation enough?
Implementation notes
  • Start with one focused feature, not a full course inside one page.
  • All public inputs must be typed, bounded, and covered by reject-case tests.
  • If a model, dataset, or job is added, document source, license, limits, and fallback.
  • The interaction must explain the topic rather than serve as decoration.