Propagating authored packages to asset management platforms

A system, comprising:

• a processor that:
• uses bitemporal modeling to track availability of an asset, wherein the bitemporal modeling includes:
• a first record associated with the availability of the asset at a first point in time, from a perspective of the first point in time;
• and
• a second record associated with the availability of the asset at the first point in time, from a perspective of a later point in time after the first point in time;
• after a second point in time has occurred, collects data reflecting an actual availability of the asset, including at the second point in time;
• compares a prediction regarding a future availability of the asset, including at the second point in time, against the data reflecting the actual availability of the asset, including at the second point in time;
• and
• after a second training stage, generates an updated package, wherein:
• at an asset management platform that manages the asset, the updated package is installed such that management of the asset by the asset management platform is modified, including one or more of the following:
• operation of the asset, maintenance of the asset, or use of the asset;
• and
• an interface to a machine learning (ML) model that:
• instructs the ML model to train on the availability of the asset that is tracked by the bitemporal modeling during a first training stage;
• after the first training stage and before the second training stage, instructs the ML model to generate the prediction regarding the future availability of the asset, including at the second point in time that has yet to occur;
• and
• instructs the ML model to further train on said comparison during the second training stage in order to correct for any discrepancies between the prediction regarding the future availability of the asset and the data reflecting the actual availability of the asset.