Abstract: Embodiments relate to providing self-learning automated information technology change risk prediction. A processor inputs a change request to a first machine learning model, the first machine learning model determining at least one word pair in the change request, the change request being a modification in an IT environment. The processor classifies the at least one word pair into a change category for the IT environment using a second machine learning model, the change category identifying a type of the modification to be executed in the IT environment. The processor determines a likelihood of causing a problem in the IT environment as a result of executing the modification. The processor automatically performs an action to prevent the modification of the change request in the IT environment.

Abstract: Embodiments relate to providing automated text summarization techniques for capturing and conveying information technology (IT) records with numerical data. A technique is executed by one or more processors and includes receiving an IT record comprising text and numerical data, normalizing the numerical data into normalized numerical data, transforming the normalized numerical data into comparative and superlative adjectival terms and rewriting the text to include the comparative and superlative adjectival terms for output as a rewritten IT record.

Abstract: Embodiments relate to early detection of information technology (IT) failures in a computing system. A technique is executed by one or more processors and includes receiving multiple IT records including past and recent historical data, extracting first and second time series sections from the past and recent historical data, respectively, training a failure detection model to correlate metric patterns in the first time series sections with at least one of other metric patterns and previous IT failures and, in response to the training, using the failure detection model to predict at least one of upcoming metric patterns and upcoming IT failures from metric patterns in the second time series sections.

Abstract: Embodiments relate to providing self-learning automated information technology change risk prediction. A processor inputs a change request to a first machine learning model, the first machine learning model determining at least one word pair in the change request, the change request being a modification in an IT environment. The processor classifies the at least one word pair into a change category for the IT environment using a second machine learning model, the change category identifying a type of the modification to be executed in the IT environment. The processor determines a likelihood of causing a problem in the IT environment as a result of executing the modification. The processor automatically performs an action to prevent the modification of the change request in the IT environment.

Abstract: Embodiments relate to providing explainable classifications with abstention using client agnostic machine learning models. A technique includes inputting, by a processor, records to a machine learning model, the records being associated with an information technology (IT) domain. The technique includes classifying, by the processor, the records with labels using the machine learning model, the machine learning model abstaining from classifying a given record in response to the given record being outside of a scope of the IT domain.

Abstract: Embodiments relate to providing explainable classifications with abstention using client agnostic machine learning models. A technique includes classifying, by a processor, a record with a label using a machine learning model, the machine learning model abstaining from classifying a given record in response to the given record being outside of a scope of an information technology (IT) domain. The processor generates an explanation of a decision by the machine learning model to classify the record with the label and displays the explanation in a human readable form.

Abstract: Methods and systems for ticket classification and response include clustering tickets according to semantic similarity to form ticket clusters. A template associated with each ticket cluster is determined that includes an invariant portion and a variable portion. A new ticket sub-class, based on the variable portion of the template, is determined that represents a specific sub-type of an existing class. A ticket taxonomy is updated to include the new ticket sub-class. The tickets are labeled according to the updated ticket taxonomy. The tickets are automatically responded to.