Module

AI, ML and Computational-Model Lifecycle

Govern datasets, feature releases, experiments, models, checkpoints, evaluations and composed releases across learned and non-learned computation.

Module responsibility

Govern datasets, feature releases, experiments, models, checkpoints, evaluations and composed releases across learned and non-learned computation.

Bind code, data, features, recipe, environment, parameters, outputs and evidence to every run.

Researchers can reproduce or explain why a result cannot be reproduced.

Version statistical, learned, mechanistic, stochastic, simulation and hybrid models with their owning semantics.

Products can discover and govern computational models without collapsing them into one ML taxonomy.

Compose checkpoints, evaluations, policy, applicability and runtime evidence into reviewable releases.

Only evidence-complete releases advance, roll back or retire through explicit state transitions.

Bind code, data, features, recipe, environment, parameters, outputs and evidence to every run.

Version statistical, learned, mechanistic, stochastic, simulation and hybrid models with their owning semantics.

Compose checkpoints, evaluations, policy, applicability and runtime evidence into reviewable releases.

Experiment and Run TrackingBind code, data, features, recipe, environment, parameters, outputs and evidence to every run. Run completion is not model acceptance.
Model RegistryVersion statistical, learned, mechanistic, stochastic, simulation and hybrid models with their owning semantics. Haephestus does not own domain mathematics or training execution.
Model and AI Release PromotionCompose checkpoints, evaluations, policy, applicability and runtime evidence into reviewable releases. Registry status cannot bypass model, policy or runtime owners.