Pruning synthetic data to manage resource cost for artificial intelligence model training
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 or replication. If positively evaluated, then alternative locations may be identified based on resources needed to operate the components or reduced cost versions of the components. The alternative locations and other locations for obtaining reduced cost versions of the components may be evaluated at least based on data transmission resource cost, and one of each location may be selected and used to update operation of the artificial intelligence workflow pipeline.
Claims20
A method for managing operation of a distributed system hosting an artificial intelligence workflow pipeline, the method comprising:
- making an identification that a trained machine learning model of the artificial intelligence workflow pipeline is at least partially impaired in its operation due to a host location of the distributed system;
- based on the identification:
- identifying a plurality of alternative host locations of the distributed system;
- identifying a model change for the trained machine learning model to be compatible with at least one of the plurality of alternative host locations;
- identifying a plurality of provisional potential support locations;
- establishing pruning plans for each of the plurality of provisional potential support locations;
- filtering the plurality of provisional potential support locations based on criteria for a new trained machine learning model based on the model change to obtain a plurality of qualified potential support locations;
- selecting one of the plurality of alternative host locations and one of the plurality of qualified potential support locations based, at least in part, on an objective function;
- migrating the trained machine learning model to the selected one of the plurality of alternative host locations using, at least in part, the selected one of the plurality of qualified potential support locations to obtain an updated artificial intelligence workload pipeline;
- and
- provisioning desired computer implemented services using the updated artificial intelligence workload 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 trained machine learning model of the artificial intelligence workflow pipeline is at least partially impaired in its operation due to a host location of the distributed system;
- based on the identification:
- identifying a plurality of alternative host locations of the distributed system;
- identifying a model change for the trained machine learning model to be compatible with at least one of the plurality of alternative host locations;
- identifying a plurality of provisional potential support locations;
- establishing pruning plans for each of the plurality of provisional potential support locations;
- filtering the plurality of provisional potential support locations based on criteria for a new trained machine learning model based on the model change to obtain a plurality of qualified potential support locations;
- selecting one of the plurality of alternative host locations and one of the plurality of qualified potential support locations based, at least in part, on an objective function;
- migrating the trained machine learning model to the selected one of the plurality of alternative host locations using, at least in part, the selected one of the plurality of qualified potential support locations to obtain an updated artificial intelligence workload pipeline;
- and
- provisioning desired computer implemented services using the updated artificial intelligence workload 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 trained machine learning model of the artificial intelligence workflow pipeline is at least partially impaired in its operation due to a host location of the distributed system;
- based on the identification:
- identifying a plurality of alternative host locations of the distributed system;
- identifying a model change for the trained machine learning model to be compatible with at least one of the plurality of alternative host locations;
- identifying a plurality of provisional potential support locations;
- establishing pruning plans for each of the plurality of provisional potential support locations;
- filtering the plurality of provisional potential support locations based on criteria for a new trained machine learning model based on the model change to obtain a plurality of qualified potential support locations;
- selecting one of the plurality of alternative host locations and one of the plurality of qualified potential support locations based, at least in part, on an objective function;
- migrating the trained machine learning model to the selected one of the plurality of alternative host locations using, at least in part, the selected one of the plurality of qualified potential support locations to obtain an updated artificial intelligence workload pipeline;
- and
- provisioning desired computer implemented services using the updated artificial intelligence workload pipeline.
Dependencies are inferred from the claim text.