AIMODELRUN

AIMODELRUN refers to a single execution instance of an artificial intelligence model, typically within an experiment or deployment workflow. It is used to capture the complete provenance of a model run, whether during training, evaluation, or inference, and to support reproducibility and auditability across the model lifecycle.

A run is identified by a unique run_id and is associated with a specific model version, dataset version, and preprocessing pipeline. It records hardware and software context (for example, CPU/GPU used, memory, operating system, library versions, and container or environment image), timing information (start and end timestamps, duration), and configuration details such as hyperparameters, random seeds, feature engineering steps, and data splits.

An AIMODELRUN also aggregates outputs and artifacts produced during the run, including loss and other metrics at each epoch or step, evaluation results on validation and test sets, trained model checkpoints or artifacts, and generated predictions or logs. It may include debugging traces, error reports, and resource usage summaries.

In practice, AIMODELRUNs are managed by experiment management and model governance tools, enabling tracking, comparability, and reproducibility across runs. They support actions such as comparing metrics across runs, reproducing experiments by re-creating the exact environment and seeds, and auditing decisions taken during model development and deployment (for example, data versioning, parameter choices, and evaluation criteria).

Common workflows include training runs, fine-tuning runs, and inference runs, as well as hyperparameter sweeps and A/B test experiments. By organizing runs with consistent metadata and artifacts, teams can improve transparency, accountability, and the ability to trace model behavior back to its inputs and settings.