Abstract: In some implementations, a query system may receive a natural language prompt from a user. The query system may convert the natural language prompt into a structured query language (SQL) query using a large language model (LLM) and schema information associated with a monitoring database. The query system may execute the SQL query against the monitoring database to receive monitoring results. The query system may convert the monitoring results into a natural language response, using the LLM, for output to the user.

Abstract: An apparatus comprises at least one processing device configured to obtain data associated with software products from a plurality of data sources, to identify association between portions of the data and respective ones of the software products, and to determine relationships between different subsets of the data obtained from different ones of the data sources based on the identified associations. The processing device is also configured to generate, for a given software product based on the determined relationships, a software product model data structure comprising portions of first and second subsets of the data obtained from different data sources. The processing device is further configured to generate, in response to a request received from a data consumer, a visualization of information related to the given software product based on the software product model data structure and role-based access rules for a given role of the data consumer.

Abstract: A method comprises receiving a request to predict an amount of at least one resource for at least one hosting instance of one or more microservices. Using one or more machine learning models, the amount of the at least one resource is predicted in response to the request. The at least one hosting instance is generated based, at least in part, on the predicted amount. In some embodiments, the at least one resource comprises, for example, a memory and/or a CPU, and the amount of the at least one resource comprises a size of the memory and/or a number of CPU core units.

Abstract: A method comprises receiving a request to predict a type and a quantity of respective ones of a plurality of resources for a computing environment. Using a multiple output classification and regression machine learning model, the type and the quantity of the respective ones of the plurality of resources are predicted in response to the request. The machine learning model is trained with a dataset comprising historical resource data corresponding to respective ones of a plurality of users.

Abstract: An example methodology includes, by a computing device, receiving information regarding a new application from another computing device and determining one or more relevant features from the information regarding the new application, the one or more relevant features influencing predictions of any potential performance issue and any potential security issue. The method also includes, by the computing device, generating, using a multi-target machine learning (ML) model, a first prediction of any potential performance issue for the new application and a second prediction of any potential security issue for the new application based on the determined one or more relevant features, and sending the first and second predictions to the another computing device.

Abstract: In some implementations, a query system may receive a natural language prompt from a user. The query system may convert the natural language prompt into a structured query language (SQL) query using a large language model (LLM) and schema information associated with a monitoring database. The query system may execute the SQL query against the monitoring database to receive monitoring results. The query system may convert the monitoring results into a natural language response, using the LLM, for output to the user.

Abstract: In some implementations, a model system may train a deep learning model to determine baseline figures and stretch figures and may train a shallow learning model to determine likelihoods associated with achieving the baseline figures and the stretch figures. The model system may receive indicators associated with a current entity and may provide the indicators to the deep learning model to generate a baseline figure and a stretch figure. The model system may receive progress data associated with the current entity. The model system may provide output based on the deep learning model and the progress data to the shallow learning model to generate a likelihood associated with the current entity achieving at least one of the baseline figure or the stretch figure. The model system may selectively provide an alert to a user based on the likelihood.

Abstract: One example method includes instantiating an ambassador component in a cloud computing environment that comprises container instances and databases, accessing, by the ambassador component, a prediction as to a number of database connections expected to be used by a particular one of the container instances, and the prediction was generated by a prediction engine, receiving, by the ambassador component, a request from the particular container instance for access to one of the databases, and providing, by the ambassador component to the particular container instance from which the request for access was received, an access object usable by that particular container instance to use a database connection to access the database identified in the request.

Abstract: One example method includes detecting an operation of an input device, obtaining a clickstream associated with a user, using information from the clickstream, generating a prediction as to a next action by the user using the input device, and presenting the prediction to the user for possible selection by the user. Selection of the prediction by the user eliminates the need for the user to perform input device manipulations that would otherwise be required if the prediction were not selected.

Abstract: A method comprises analyzing metrics corresponding to operation of a first node in a first data center using at least one machine learning algorithm, predicting, based at least in part on the analyzing of the metrics, whether the operation of the first node is anomalous, designating the first node as being in an anomalous state responsive to predicting that the operation of the first node is anomalous, and causing routing of one or more database transactions to a second node in a second data center instead of the first node in response to the anomalous state designation.

Abstract: A method comprises receiving a request for cloud deployment of at least one microservice, wherein the request includes one or more features of the at least one microservice. The one or more features are analyzed using one or more machine learning algorithms. The method further comprises predicting, based at least in part on the analyzing: (i) a cloud platform of a plurality of cloud platforms to deploy the at least one microservice; and (ii) a cloud instance in which the at least one microservice is to be executed, and interfacing with the cloud platform to enable deployment of the at least one microservice.

Abstract: A method comprises analyzing, using one or more machine learning algorithms, parameters corresponding to at least one microservice operation processed by a first instance of a microservice, and predicting, based at least in part on the analyzing, whether the at least one microservice operation is anomalous. The first instance of the microservice is designated as being in an anomalous state responsive to predicting that the at least one microservice operation is anomalous. One or more requests for the microservice are routed to a second instance of the microservice responsive to the anomalous state designation. The method further comprises causing logging of information corresponding to operation of the second instance of the microservice to be enabled at a designated level of granularity.

Abstract: A method comprises receiving a request to predict a deployment configuration for at least one application, analyzing code of the at least one application to identify one or more additional applications on which the at least one application will depend, identifying a plurality of network paths between the at least one application and the one or more additional applications, and using one or more machine learning algorithms to predict execution times for the at least one application over the plurality of network paths. The predicted execution times for the at least one application over the plurality of network paths are inputted to a network graph model. The network graph model predicts the deployment configuration for the at least one application based at least in part on the predicted execution times, wherein the deployment configuration comprises a subset of the plurality of network paths.

Abstract: A method comprises receiving a large language model request, analyzing the large language model request using one or more machine learning algorithms, and predicting, based at least in part on the analyzing: (i) a large language model of a plurality of large language models to process and to respond to the large language model request; and (ii) at least one database from which data is to be used to generate a prompt for the large language model. The method further comprises interfacing with the large language model and the at least one database to enable the large language model to process and to respond to the large language model request.

Abstract: An apparatus comprises at least one processing device configured to determine interdependencies between a first and one or more additional applications developed by a given entity and mappings between the first and one or more additional applications and a training hierarchy comprising a plurality of trainings. The at least one processing device is also configured to identify a given user role of a given user responsible for development of the first application, and to generate, utilizing one or more machine learning models that take as input the given user role, the determined interdependencies and the determined mappings, a training plan for the given user specifying trainings to be completed by the given user. The at least one processing device is further configured to track a progress of the training plan by the given user, and to dynamically update a user profile of the given user based on the tracked progress.

Abstract: A method comprises receiving a request for integration of a plurality of computing instances, wherein the request comprises a plurality of features for the integration, and implementing a plurality of services based at least in part on the plurality of features, wherein at least one service of the plurality of services comprises at least one code generation service. Code for the integration is generated using the at least one code generation service. The generated code for the integration is pushed to at least one development and information technology operations platform.

Abstract: A method comprises collecting data corresponding to a plurality of components in a system, wherein the data comprises information about one or more issues with the plurality of components. The data is analyzed and categorized based at least in part on the analysis. In the method, one or more application programming interfaces (APIs) are selected to monitor respective statuses of the plurality of components, wherein the selection is based at least in part on the categorization of the data, and the data is pushed to the one or more APIs.

Abstract: A method comprises identifying a request for data, and identifying one or more data elements that are responsive to the request for data. The one or more data elements are analyzed to classify whether the one or more data elements comprise personally identifiable information, wherein the analyzing is performed using one or more machine learning models. The method further comprises interfacing with one or more cloud platforms of a plurality of cloud platforms to transfer the one or more data elements that have been classified as comprising personally identifiable information to the one or more cloud platforms.

Abstract: A method comprises receiving a request to predict a configuration of a cloud instance in which at least one application is to be executed, wherein the request includes one or more features of the at least one application. The one or more features are analyzed using one or more machine learning algorithms. Based at least in part on the analyzing, the configuration of a cloud instance in which at least one application is to be executed is predicted. The configuration comprises an amount of utilization for one or more computer resources in connection with execution of the at least one application in the cloud instance.

Abstract: A method comprises analyzing a plurality of natural language inputs associated with at least one user, and determining a plurality of contexts for the plurality of natural language inputs based, at least in part, on the analysis. In the method, a plurality of relationships linked to the at least one user are identified based, at least in part, on the analysis, and the at least one user is classified in one or more categories based, at least in part, on the plurality of contexts and the plurality of relationships. At least one of the analyzing, determining, identifying and classifying is performed using one or more machine learning models.