A method of identifying a contributing cause of an anomaly including receiving a set of timeseries data representing metric values over time, wherein the timeseries data has at least two dimensions, for each of two or more of the timestamps in the set of timeseries data, generating a first and second graph representing (i) the metric values at that timestamp, (ii) the at least two dimensions at that timestamp, and (iii) associations between the metric values at that timestamp, analyzing the first and second graphs associated with each timestamp to identify a particular timestamp including an anomaly, and analyzing the first and second graphs associated with the identified particular timestamp to identify a node that contributed in causing the anomaly.

Systems and/or techniques for facilitating online-monitoring of machine learning models are provided. In various embodiments, a system can receive monitoring settings associated with a machine learning model to be monitored. In various cases, the monitoring settings can identify a first set of data features that are generated as output by the machine learning model. In various cases, the monitoring settings can identify a second set of data features that are received as input by the machine learning model. In various aspects, the system can compute a first set of statistical metrics based on the first set of data features. In various cases, the first set of statistical metrics can characterize a performance quality of the machine learning model. In various instances, the system can compute a second set of statistical metrics based on the second set of data features. In various cases, the second set of statistical metrics can characterize trends or distributions of input data associated with the machine learning model. In various aspects, the system can store the first set of statistical metrics and the second set of statistical metrics in a data warehouse that is accessible to an operator. In various embodiments, the system can render the first set of statistical metrics and the second set of statistical metrics on an electronic interface, such that the first set of statistical metrics and the second set of statistical metrics are viewable to the operator.

A system and related method identify a weakness of a workflow in a complex system. The method collects runtime data about the complex system. The complex system comprises a plurality of subcomponents, and the method identifies an abnormal operation in the complex system. The method constructs a multi-dimensional cause-and-effect relation matrix among the plurality of subcomponents, and filters one or more related operations using the multi-dimensional cause-and-effect relation matrix.

A hardware monitor arranged to assess performance of a hardware design for an integrated circuit to complete a task. The hardware monitor includes monitoring and counting logic configured to count a number of cycles between start and completion of the symbolic task in an instantiation of the hardware design; and property evaluation logic configured to evaluate one or more formal properties related to the counted number of cycles to assess the performance of the instantiation of the hardware design in completing the symbolic task. The hardware monitor may be used by a formal verification tool to exhaustively verify that the hardware design meets a desired performance goal and/or to exhaustively identify a performance metric (e.g. best case and/or worst case performance) with respect to completion of the task.

Systems for optimized forecasting are provided. In some examples, data associated with strategy of one or more business units may be received. The strategy data may include identification of projects or goals. In some examples, industry trend data may be received and may include data associated with in-demand job skills and the like. An instruction to capture user data may be transmitted to one or more user devices of an employee user. The instruction may cause activation of one or more sensors or data capture devices. The captured user data may be received and analyzed to determine a competency of the user. Based on the strategy data, industry data and determined competency, one or more deficiencies between the resources needed to meet the business unit strategy data and the available resources may be identified. Based on the identified deficiency, one or more actions for execution may be identified and executed.

In some examples, just-in-time (JIT) control instructions upon execution cause a system to initiate a plurality of instances of JIT compilation of a first code called by a program, where the initiating of the plurality of instances of the JIT compilation of the first code is under control of the JIT control instructions that are outside the program, and the plurality of instances of the JIT compilation of the first code use respective different compilation settings, and are to produce respective JIT compiled instances of the first code.

Aspects of the disclosure relate to managing a set of warning data with respect to a development phase in a computing environment. In embodiments, the computing environment may include a distributed computing environment or a stream computing environment. The set of warning data may be detected with respect to the development phase. In embodiments, the set of warning data may be used to develop an application. By analyzing the set of warning data, a relationship between the set of warning data and a component of the application may be identified. In embodiments, the component of the application may include a computing artifact or a computing object. An indication of the relationship between the set of warning data and the component of the application may be provided for utilization to develop the application.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for automatically classifying static analysis rules as being anomalous or not. One of the methods includes receiving alerts generated by a particular static analysis rule for a plurality of different software projects analyzed by a static analysis system. For each project, a respective alert proportion metric value is computed. Each of the plurality of different software projects is classified according to the alert proportion metric values as being one non-outlier projects or outlier projects. If more than a threshold number of projects were classified as being outlier projects for the particular static analysis rule, the particular static analysis rule is classified as an anomalous static analysis rule.

Techniques for recommending microservices to perform the different functions of a legacy architecture are disclosed. In one example, a computer implemented method comprises receiving a plurality of recommendations comprising a plurality of program components as candidates for assignment to a plurality of microservices, and determining roles of respective ones of the plurality of program components. A user interface is provided which is configured to allow a user to modify one or more of the plurality of recommendations based at least in part on the roles. Modifications to the one or more of the plurality of recommendations are analyzed, and one or more metrics are computed based at least in part on the analysis.

A method for autonomous vehicle test data distribution and analysis is described. The method includes uploading driving session data from a computer of a drive site to a network attached storage of the drive site. The method also includes uploading the driving session data from the network attached storage of the drive site to a cloud-based storage location. The method further includes distributing the driving session data from the cloud-based storage location and a work unit to at least one research site separate from the drive site. The method also includes processing, by the at least one research site, the driving session data according to an analysis/processing task associated with the work unit.