Predictive maintenance for terminals

A method, comprising:

• training machine learning models (MLMs) to predict terminal failures of terminals;
• testing a capability of each MLM to provide correct predictions;
• calculating respective prediction scores for the MLMs based on the testing;
• selecting an MLM having a highest prediction score from the calculated prediction scores;
• providing the selected MLM to a deployment environment to provide predictions of failures of the terminals during a current period of time;
• obtaining candidate predictions of failures of the terminals from non-selected MLMs during the current period of time;
• calculating new prediction scores for the selected MLM and the non-selected MLMs;
• selecting a next MLM for a next period of time based on the new prediction scores;
• and
• providing the selected next MLM to the deployment environment to provide predictions of failures from the terminals during the next period of time.

A system, comprising:

• a service comprising at least one processor and a non-transitory computer-readable storage medium, the non-transitory computer-readable storage medium comprising executable instructions that when provided to or obtained by the at least one processor from the non-transitory computer-readable storage medium cause the at least one processor to perform operations, comprising:
• obtaining historical training data associated with terminal failures and non-failures of terminals;
• sampling and resampling the historical training data to generate a balanced data set of failures and non-failures;
• labeling features within the data set, the features comprising terminal identifiers for the terminals, the failures, the non-failures, dates for the failures, and remediation actions taken for the failures;
• portioning the data set into training data and testing data;
• training a plurality of machine learning models (MLMs) based on the training data, the plurality of MLMs including two or more types of MLMs;
• testing the plurality of types of MLMs based on the testing data;
• calculating a respective score for each type of MLM based on the testing;
• selecting an MLM having a highest score;
• deploying the selected MLM into a production environment of a retailer as a deployed MLM to provide daily predictions on potential terminal failures over a current period of time;
• calculating new scores for the deployed MLM and non-deployed MLMs not selected for operation within the production environment at the end of the current period of time;
• selecting a next MLM having a highest score for a next period of time;
• training the next selected MLM on most-recent training data over a most-recent training period of time;
• deploying the next MLM into the production environment as the deployed MLM to provide the daily predictions during the next period of time;
• and
• continuously iterating to recalculate the scores at the end of each next period of time.