MLOps / experiment-management

Experiment management

Reproducibility, seeds, deterministic training, experiment tracking, artifacts, model registry, config management, and tuning.

shellbackend needed later
Focus
  • reproducibility
  • tracking
  • artifact storage
  • hyperparameter tuning
Future feature idea

Experiment tracker demo with seeds, configs, metrics, artifacts, and hyperparameter tuning.

Self-check
  1. What is the core job of "Experiment management"?
  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.