US Patent 11481584 Efficient machine learning (ML) model for classification

Techniques for implementing an efficient machine learning (ML) model for classification are provided. In one set of embodiments, a computer system can receive a query data instance to be classified. The computer system can then generate a first classification result for the query data instance using a first (i.e., primary) ML model, where the first classification result includes a predicted class for the query data instance and a confidence level indicating a likelihood that the predicted class is correct, and compare the confidence level with a classification confidence threshold. If the confidence level is greater than or equal to the classification confidence threshold, the computer system can output the first classification result as a final classification result for the query data instance. However, if the confidence level is less than the classification confidence threshold, the computer system can forward the query data instance to one of a plurality of second (i.e., secondary) ML models for further classification.