Iterative training of a machine learning model

A computer program product comprising one or more computer readable storage media and program instructions collectively stored on the one or more computer readable storage media, the stored program instructions comprising:

• program instructions to train a machine-learning model using an initial set of training data samples;
• program instructions to receive a new training data sample;
• program instructions to predict a label for the new training data sample;
• program instructions to, upon determining that a prediction quality value for the predicted label of the new training data sample is below a predefined quality value, executing the actions comprising:
• program instructions to add the new training data sample to the initial set, thereby building an extended training data set;
• program instructions to reconfirm the predicted label of the new training data sample by applying a self-supervised learning method to the new training data sample;
• and
• program instructions to retrain the machine-learning model using the extended training data set;
• and
• program instructions to, upon determining that the prediction quality value for the predicted label of the new training data sample cannot be improved, trigger an alarm event.

A computer system comprising:

• a processor(s) set;
• and
• a computer readable storage medium;
• wherein:
• the processor(s) set is structured, located, connected and/or programmed to run program instructions stored on the computer readable storage medium;
• and
• the stored program instructions comprise:
• program instructions to train a machine-learning model using an initial set of training data samples;
• program instructions to receive a new training data sample;
• program instructions to predict a label for the new training data sample;
• program instructions to, upon determining that a prediction quality value for the predicted label of the new training data sample is below a predefined quality value, executing the actions comprising:
• program instructions to add the new training data sample to the initial set, thereby building an extended training data set;
• program instructions to reconfirm the predicted label of the new training data sample by applying a self-supervised learning method to the new training data sample;
• and
• program instructions to retrain the machine-learning model using the extended training data set;
• and
• program instructions to, upon determining that the prediction quality value for the predicted label of the new training data sample cannot be improved, trigger an alarm event.