Selective model modification and for artificial intelligence workloads
AssigneeDell Products L.P.
InventorDHARMESH M. PATEL
Life of this patent
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Abstract
Methods, systems, and devices are provided for managing operation of a system. To manage the system, components of an artificial intelligence workflow pipeline may be evaluated for migration, replication, or tuning. If a new model instance is to be instantiated based on the evaluation, a selection process may be performed to identify a host location. The selection process may take into account resource costs for modifying the model for compatibility and useful life estimated for the new instance.
Claims20
A method for managing operation of a distributed system hosting an artificial intelligence workflow pipeline, the method comprising:
- making an identification that a new instance of a trained machine learning model is to be added to the distributed system for the artificial intelligence workflow pipeline;
- identifying potential host locations of the distributed system for the new instance;
- estimating resource costs for compatibility of the trained machine learning model with the respective potential host locations;
- estimating a useful life of the new instance;
- selecting one of the potential host locations based, at least in part, on the resource costs and the useful life;
- instantiating the new instance of the trained machine learning model to the selected one of the potential host locations to obtain an updated artificial intelligence workflow pipeline;
- and
- provisioning desired computer implemented services using the updated artificial intelligence workflow pipeline.
A non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause operations for managing a distributed system hosting an artificial intelligence workflow pipeline to be performed, the operations comprising:
- making an identification that a new instance of a trained machine learning model is to be added to the distributed system for the artificial intelligence workflow pipeline;
- identifying potential host locations of the distributed system for the new instance;
- estimating resource costs for compatibility of the trained machine learning model with the respective potential host locations;
- estimating a useful life of the new instance;
- selecting one of the potential host locations based, at least in part, on the resource costs and the useful life;
- instantiating the new instance of the trained machine learning model to the selected one of the potential host locations to obtain an updated artificial intelligence workflow pipeline;
- and
- provisioning desired computer implemented services using the updated artificial intelligence workflow pipeline.
A system, comprising:
- a processor;
- and
- a memory coupled to the processor to store instructions, which when executed by the processor, cause operations for managing a distributed system hosting an artificial intelligence workflow pipeline to be performed, the operations comprising:
- making an identification that a new instance of a trained machine learning model is to be added to the distributed system for the artificial intelligence workflow pipeline;
- identifying potential host locations of the distributed system for the new instance;
- estimating resource costs for compatibility of the trained machine learning model with the respective potential host locations;
- estimating a useful life of the new instance;
- selecting one of the potential host locations based, at least in part, on the resource costs and the useful life;
- instantiating the new instance of the trained machine learning model to the selected one of the potential host locations to obtain an updated artificial intelligence workflow pipeline;
- and
- provisioning desired computer implemented services using the updated artificial intelligence workflow pipeline.
Dependencies are inferred from the claim text.