Machine learning hyper tuning

A method comprising:

• an information handling system communicatively coupling to a cloud platform for execution of a machine learning task;
• and
• the information handling system causing the cloud platform to execute a hyper server that is configured to:
• determine a plurality of sets of possible values for hyperparameters of the machine learning task;
• for each of the plurality of sets of possible values, dispatch a model comprising the set to a hyper client configured to execute a model training process based on the set;
• receive, for each set, statistics relating to the model training process for the set;
• and
• determine, based on the received statistics, a particular set that is preferred.

An article of manufacture comprising a non-transitory, computer-readable medium having computer-executable code thereon that is executable by an information handling system for:

• communicatively coupling to a cloud platform for execution of a machine learning task;
• and
• causing the cloud platform to execute a hyper server that is configured to:
• determine a plurality of sets of possible values for hyperparameters of the machine learning task;
• for each of the plurality of sets of possible values, dispatch a model comprising the set to a hyper client configured to execute a model training process based on the set;
• receive, for each set, statistics relating to the model training process for the set;
• and
• determine, based on the received statistics, a particular set that is preferred.