Abstract: An example system can provide intelligent continuous learning by updating a machine learning model based on a new dataset. The system can utilize a transfer loss function that does not depend on old datasets used to train the existing model. The system can receive, on a graphical user interface (“GUI”), a selection of configuration criteria including threshold performance for automatic deployment. The new model can be created iteratively based on the configuration criteria. An evaluation of the new model over multiple iterations can be presented on the GUI. In an instance where the new model meets a deployment requirement selected on the GUI, a server can deploy the new model in place of the existing model.

Abstract: Systems and methods can implement a review process to evaluate changes to target code as part of development cycles for a continuous integration, continuous deployment pipeline for software-based products. The system can aggregate data and determine if the target code has been modified preliminarily and then intelligently determine where further review is needed before the changes are permanently implemented. To do this, a changeset including the preliminarily changed target code can be obtained from the aggregated data. The changeset can be tested with a prediction model based on feature data that characterizes aspects of a coding process carried out to generate the preliminary modification. The prediction model can provide an activation recommendation for the preliminary modification based on a plurality of risk factors determined from the testing. The prediction model can be trained, continuously, with training data that includes a plurality of data artifacts resulting from a code build processes.

Abstract: Examples described herein include systems and methods performing scalable and dynamic data processing and extraction. A first example method relates to processing events from a source. The method can include detecting an event generated by the source and predicting a probability of that event being part of a span including multiple events. The method can include waiting for the additional multiple events to occur within the predicted timeframe and, if occurring, packaging the events together for handling by a single dynamic function. Otherwise, the events can each be handled by separate dynamic functions. A second example method relates to performing dynamic data extraction from a source. The method can include waking up a function based on a regular poll interval, determining a probability of a data change at the source based on historical data extractions, and invoking an extraction function based on the probability of the data change.

Abstract: Examples described herein include systems and methods performing scalable and dynamic data processing and extraction. A first example method relates to processing events from a source. The method can include detecting an event generated by the source and predicting a probability of that event being part of a span including multiple events. The method can include waiting for the additional multiple events to occur within the predicted timeframe and, if occurring, packaging the events together for handling by a single dynamic function. Otherwise, the events can each be handled by separate dynamic functions. A second example method relates to performing dynamic data extraction from a source. The method can include waking up a function based on a regular poll interval, determining a probability of a data change at the source based on historical data extractions, and invoking an extraction function based on the probability of the data change.

Abstract: An example system can provide intelligent continuous learning by updating a machine learning model based on a new dataset. The system can utilize a transfer loss function that does not depend on old datasets used to train the existing model. The system can receive, on a graphical user interface (“GUI”), a selection of configuration criteria including threshold performance for automatic deployment. The new model can be created iteratively based on the configuration criteria. An evaluation of the new model over multiple iterations can be presented on the GUI. In an instance where the new model meets a deployment requirement selected on the GUI, a server can deploy the new model in place of the existing model.

Abstract: Systems and methods can implement a review process to evaluate changes to target code as part of development cycles for a continuous integration, continuous deployment pipeline for software-based products. The system can aggregate data and determine if the target code has been modified preliminarily and then intelligently determine where further review is needed before the changes are permanently implemented. To do this, a changeset including the preliminarily changed target code can be obtained from the aggregated data. The changeset can be tested with a prediction model based on feature data that characterizes aspects of a coding process carried out to generate the preliminary modification. The prediction model can provide an activation recommendation for the preliminary modification based on a plurality of risk factors determined from the testing. The prediction model can be trained, continuously, with training data that includes a plurality of data artifacts resulting from a code build processes.

Abstract: Systems and methods can implement a review process to evaluate changes to target code as part of development cycles for a continuous integration, continuous deployment pipeline for software-based products. The system can aggregate data and determine if the target code has been modified preliminarily and then intelligently determine where further review is needed before the changes are permanently implemented. To do this, a changeset including the preliminarily changed target code can be obtained from the aggregated data. The changeset can be tested with a prediction model based on feature data that characterizes aspects of a coding process carried out to generate the preliminary modification. The prediction model can provide an activation recommendation for the preliminary modification based on a plurality of risk factors determined from the testing. The prediction model can be trained, continuously, with training data that includes a plurality of data artifacts resulting from a code build processes.

Abstract: Examples described herein include systems and methods performing scalable and dynamic data processing and extraction. A first example method relates to processing events from a source. The method can include detecting an event generated by the source and predicting a probability of that event being part of a span including multiple events. The method can include waiting for the additional multiple events to occur within the predicted timeframe and, if occurring, packaging the events together for handling by a single dynamic function. Otherwise, the events can each be handled by separate dynamic functions. A second example method relates to performing dynamic data extraction from a source. The method can include waking up a function based on a regular poll interval, determining a probability of a data change at the source based on historical data extractions, and invoking an extraction function based on the probability of the data change.

Abstract: Examples described herein include systems and methods performing scalable and dynamic data processing and extraction. A first example method relates to processing events from a source. The method can include detecting an event generated by the source and predicting a probability of that event being part of a span including multiple events. The method can include waiting for the additional multiple events to occur within the predicted timeframe and, if occurring, packaging the events together for handling by a single dynamic function. Otherwise, the events can each be handled by separate dynamic functions. A second example method relates to performing dynamic data extraction from a source. The method can include waking up a function based on a regular poll interval, determining a probability of a data change at the source based on historical data extractions, and invoking an extraction function based on the probability of the data change.

Abstract: Systems and methods can implement a review process to evaluate changes to target code as part of development cycles for a continuous integration, continuous deployment pipeline for software-based products. The system can aggregate data and determine if the target code has been modified preliminarily and then intelligently determine where further review is needed before the changes are permanently implemented. To do this, a changeset including the preliminarily changed target code can be obtained from the aggregated data. The changeset can be tested with a prediction model based on feature data that characterizes aspects of a coding process carried out to generate the preliminary modification. The prediction model can provide an activation recommendation for the preliminary modification based on a plurality of risk factors determined from the testing. The prediction model can be trained, continuously, with training data that includes a plurality of data artifacts resulting from a code build processes.