Machine learning-based processing of logs containing unstructured text data

A computer program product comprising a non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device:

• to obtain a plurality of logs associated with operation of one or more information technology assets in an information technology infrastructure environment, the plurality of logs comprising unstructured text data characterizing one or more issues encountered on the one or more information technology assets in the information technology infrastructure environment;
• to generate, utilizing a transformer-based machine learning model, embeddings for the plurality of logs;
• to select, based at least in part on one or more cluster validation metrics determined by processing the embeddings for the plurality of logs, a target number of clusters;
• to determine, utilizing a machine learning-based clustering model that takes as input the target number of clusters, a set of clusters for the plurality of logs;
• to determine a prioritization of the one or more issues encountered on the one or more information technology assets in the information technology infrastructure environment based at least in part on identifying associations between respective ones of the clusters in the determined set of clusters and at least one of (i) applications executing on the one or more information technology assets in the information technology infrastructure environment and (ii) users of the applications executing on the one or more information technology assets in the information technology infrastructure environment;
• and
• to remediate the one or more issues encountered on the one or more information technology assets in the information technology infrastructure environment based at least in part on the determined prioritization.

A method comprising:

• obtaining a plurality of logs associated with operation of one or more information technology assets in an information technology infrastructure environment, the plurality of logs comprising unstructured text data characterizing one or more issues encountered on the one or more information technology assets in the information technology infrastructure environment;
• generating, utilizing a transformer-based machine learning model, embeddings for the plurality of logs;
• selecting, based at least in part on one or more cluster validation metrics determined by processing the embeddings for the plurality of logs, a target number of clusters;
• determining, utilizing a machine learning-based clustering model that takes as input the target number of clusters, a set of clusters for the plurality of logs;
• determining a prioritization of the one or more issues encountered on the one or more information technology assets in the information technology infrastructure environment based at least in part on identifying associations between respective ones of the clusters in the determined set of clusters and at least one of (i) applications executing on the one or more information technology assets in the information technology infrastructure environment and (ii) users of the applications executing on the one or more information technology assets in the information technology infrastructure environment;
• and
• remediating the one or more issues encountered on the one or more information technology assets in the information technology infrastructure environment based at least in part on the determined prioritization;
• wherein the method is performed by at least one processing device comprising a processor coupled to a memory.