Embodiments for implementing mixed precision learning for neural networks by a processor. A neural network may be replicated into a plurality of replicated instances and each of the plurality of replicated instances differ in precision used for representing and determining parameters of the neural network. Data instances may be routed to one or more of the plurality of replicated instances for processing according to a data pre-processing operation.

A method and system for detecting anomalies in mission-critical environments using word representation learning are provided. The method includes parsing at least one received data set into a text structure; isolating a protocol language of the at least one received data set, wherein the protocol language is a standardized pattern for communication over at least one communication protocol; generating at least one document from the contents of the received at least one data set, wherein the at least one document includes at least one parsed text structure referencing a unique identifier; detecting insights in the at least one generated document, wherein insights are detected in at least one representation having at least one dimension, wherein the representation is mapped to at least one learned hyperspace; extracting rules from the detected insights; and detecting anomalies by applying the extracted rules on patterns for communication over at least one communication protocol.

Techniques are provided for extracting anomaly related rules from organizational data. One method comprises obtaining anomaly analysis data integrated from multiple data sources of an organization, wherein the multiple data sources comprise at least one set of labeled anomaly data related to anomalous transactions; extracting features from the integrated anomaly analysis data that correlate with an indication of an anomaly; training multiple machine learning models using the extracted features, where the machine learning models are trained using different combinations of the extracted features; evaluating a performance of the trained machine learning models; and extracting rules from the trained machine learning models based on the performance, wherein the extracted rules are used to classify transactions as anomalous. The trained machine learning models comprise a decision tree comprising paths to an anomaly classification. The extracted rules are optionally in a human-readable format.

Embodiments of the disclosure relate to systems and methods for leveraging unsupervised machine learning to produce interpretable routing rules. In various embodiments, a training dataset comprising a plurality of data records is created. The plurality of data records includes message data comprising a plurality of messages and action data comprising a plurality of actions that correspond to the plurality of messages. A first machine learning model is trained using the training dataset. The first machine learning model as trained provides cluster data that indicates, for each data record of the plurality of data records of the training dataset, membership in a cluster of a plurality of clusters. An enhanced training dataset is created that comprises the message data from the training dataset, the action data from the training dataset, and the cluster data. A set of second machine learning models is trained using the enhanced training dataset, each respective second machine learning model of the set of second machine learning models providing a decision tree of a plurality of decision trees and corresponding to a distinct cluster of the plurality of clusters. Rules can be extracted from each decision tree of the plurality of decision trees and used as a basis for creating and transmitting alerts based on incoming messages.

Systems and methods for facilitating an automated observability of a ML model are disclosed. A system may include a processor including a model creator and a monitoring engine. The model creator may generate a configuration artifact based on a pre-defined template and a pre-defined input. The configuration artifact may pertain to expected attributes of the ML model to be created. The model creator may generate the ML model based on the configuration artifact. The monitoring engine may monitor a model attribute associated with each ML model based on monitoring rules stored in a rules engine. This may facilitate to identify an event associated with alteration in the model attribute from a pre-defined value. Based on the identified event, the system may execute an automated response including at least one of an alert and a remedial action to mitigate the event.

Systems and methods for facilitating an automated observability of a ML model are disclosed. A system may include a processor including a model creator and a monitoring engine. The model creator may generate a configuration artifact based on a pre-defined template and a pre-defined input. The configuration artifact may pertain to expected attributes of the ML model to be created. The model creator may generate the ML model based on the configuration artifact. The monitoring engine may monitor a model attribute associated with each ML model based on monitoring rules stored in a rules engine. This may facilitate to identify an event associated with alteration in the model attribute from a pre-defined value. Based on the identified event, the system may execute an automated response including at least one of an alert and a remedial action to mitigate the event.

A system includes a processing system and a memory system. The memory system stores instructions for identifying a cloud application in a cloud environment as a non-disposable application and monitoring a plurality of instances of the non-disposable application running in the cloud environment. The instructions when executed by the processing system further result in determining that a number of the instances of the non-disposable application should be modified based on one or more demand predictions by an artificial intelligence scaler, adjusting the number of the instances of the non-disposable application running in the cloud environment based on the one or more demand predictions, and modifying an allocation of one or more resources of the cloud environment associated with adjusting the number of the instances of the non-disposable application.

Techniques facilitating causal reasoning for explanation of model predictions are provided. A system can generate one or more explanations of a machine learning model prediction. The one or more explanations can be based on causal relationships determined between feature data of a set of feature data and based on dataset point samples around a trace associated with the causal relationships.

Methods, systems, and computer-readable storage media for receiving a record including a set of attributes, each attribute having an attribute value, the record representing automatic execution of an IT process within a managed system, retrieving a model representing historical executions of the IT process and including a set of distribution parameters associated with a first type of attribute and a set of probability distributions associated with a second type of attribute, determining, for a first attribute, a first score based on distribution parameters and a value, determining, for a second attribute, a second score based on a probability distribution and a value, the second attribute being of the second type of attribute, and selectively indicating that the IT process is anomalous based on an outlier score.

Systems and methods are provided to recommend software patches based on task operation mapping. In embodiments, a method includes abstracting test cases for a software patch into a sequence of task operations and parameters associated with each task operation; encoding the task operations and the parameters associated with each task operation based on predetermined rules, thereby generating encoded task operations with unique identifiers assigned thereto and associated encoded parameters with numeric values assigned thereto; generating, using machine learning, a list of frequent operation items, based on the encoded task operations and the associated encoded parameters; generating, using clustering, clusters of parameters for each frequent operation item in the list of frequent operation items; and sending a software patch package including the list of frequent operation items, the clusters of parameters and the software patch to a remote server for distribution to one or more user devices.