Abstract: A system can monitor applications and analyze the metrics to determine if one or more of the applications are regressing or performing as expected. The metric analysis includes performing a first short term data analysis and, if data is not as expected, a second short term analysis based on machine learning-based pattern recognition machines. If the short-term analysis finds the metrics aren't as expected, a long-term analysis is performed. The long-term analysis can compare chunks of streaming metric data to cached metric blocks and historical data, and can include a concept drift analysis.

Abstract: The present system uses delegates installed in remote environments to called and transmit, to a remote manager, time series metric data (or data from which metrics can be determined) in real-time. The numerical time series data is persisted, and a learned representation is generated from the data, for example by discretization. The learned representation is then clustered, the clusters are compared to new data, anomalies are determined, and deviation scores are calculated for the anomalies. The derivation scores are compared to thresholds, and results are reported through, for example, a user interface, dashboard, and/or other mechanism.

Abstract: The present system provides continuous delivery and service regression detection in real time based on log data. The log data is clustered based on textual and contextual similarity and can serve as an indicator for the behavior of a service or application. The clusters can be augmented with the frequency distribution of its occurrences bucketed at a temporal level. Collectively, the textual and contextual similarity clusters serve as a strong signature (e.g., learned representation) of the current service date and a strong indicator for predicting future behavior. Machine learning techniques are used to generate a signature from log data to represent the current state and predict the future behavior of the service at any instant in time.

Abstract: The present system uses delegates installed in remote environments to called and transmit, to a remote manager, time series metric data (or data from which metrics can be determined) in real-time. The numerical time series data is persisted, and a learned representation is generated from the data, for example by discretization. The learned representation is then clustered, the clusters are compared to new data, anomalies are determined, and deviation scores are calculated for the anomalies. The derivation scores are compared to thresholds, and results are reported through, for example, a user interface, dashboard, and/or other mechanism.