Fast, fully automatic, data-centric out-of-the-box AI model building

A computer system comprising:

• one or more processors;
• and
• a non-transitory computer-readable storage medium having instructions encoded thereon that, when executed by a processor, cause the one or more processors to:
• access a training dataset comprising a plurality of labeled samples, each of the plurality of labeled samples labeled with a ground-truth label;
• divide the plurality of labeled samples into a training subset and a holdout test subset;
• train an AI model using the training subset;
• for each of a plurality of training epochs, checkpoint while training the AI model, wherein checkpointing comprises:
• apply a partially trained AI model, which is partially trained from one or more prior training epochs, to the training subset to determine a train loss value;
• and
• apply the partially trained AI model to the holdout test subset to determine a test loss value;
• determine whether both train loss values and test loss values converge at a particular epoch;
• responsive to determining that convergence occurred at the particular epoch, select the AI model trained at the particular epoch;
• responsive to determining that convergence of train loss values and convergence of test loss values did not occur at a same epoch,
• identifying an epoch corresponding to a lowest test loss value;
• determining whether a difference between the train loss value and the test loss value at the epoch is below a margin;
• responsive to determining that the difference between the train loss value and the test loss value is below the margin, selecting the AI model trained at the epoch for use;
• and
• responsive to determining that the difference between the train loss value and the test loss value is greater than the margin, continuing on to a next training epoch.

A non-transitory computer-readable storage medium having instructions encoded thereon that, when executed by a processor, cause one or more processors to:

• access a training dataset comprising a plurality of labeled samples, each of the plurality of labeled samples labeled with a ground-truth label;
• divide the plurality of labeled samples into a training subset and a holdout test subset;
• train an AI model using the training subset;
• for each of a plurality of training epochs, checkpoint while training the AI model, wherein checkpointing comprises:
• apply a partially trained AI model, which is partially trained from one or more prior training epochs, to the training subset to determine a train loss value;
• and
• apply the partially trained AI model to the holdout test subset to determine a test loss value;
• determine whether both train loss values and test loss values converge at a particular epoch;
• responsive to determining that convergence occurred at the particular epoch, select the AI model trained at the particular epoch;
• responsive to determining that convergence of train loss values and convergence of test loss values did not occur at a same epoch,
• identifying an epoch corresponding to a lowest test loss value;
• determining whether a difference between the train loss value and the test loss value at the epoch is below a margin;
• responsive to determining that the difference between the train loss value and the test loss value is below the margin, selecting the AI model trained at the epoch for use;
• and
• responsive to determining that the difference between the train loss value and the test loss value is greater than the margin, continuing on to a next training epoch.