SERVICE LABELING USING SEMI-SUPERVISED LEARNING

The disclosure provides an approach for workload labeling and identification of known or custom applications. Embodiments include determining a plurality of sets of features comprising a respective set of features for each respective workload of a first subset of a plurality of workloads. Embodiments include identifying a group of workloads based on similarities among the plurality of sets of features. Embodiments include receiving label data from a user comprising a label for the group of workloads. Embodiments include associating the label with each workload of the group of workloads to produce a training data set. Embodiments include using the training data set to train a model to output labels for input workloads. Embodiments include determining a label for a given workload of the plurality of workloads by inputting features of the given workload to the model.