Vmware, Inc.

Dinesh Babu Thirukondan Gnaneswaran

Aishwary Thakur

Ayesha Karim

Gaurav Gupta

Reghuram Vasanthakumari

Hemanth Kumar Kilari

Subrahmanyam Mannam

Naveen Adarsh Petla

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.

Machine learning with an intelligent continuous learning service in a big data environment

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.

Review process for evaluating changes to target code for a software-based product

Techniques disclosed herein provide an approach for identifying application dependencies in a hybrid environment in which one or more applications run in operating system (OS)-less containers. One embodiment provides a computer-implemented method that includes monitoring network traffic at one or more host computer systems, wherein OS-less containers run in at least one of the host computer systems. The method further includes monitoring network traffic at virtual bridges to which the OS-less containers are attached, and identifying network dependencies based on the monitored network traffic at the host computer systems and the monitored network traffic at the virtual bridges. In addition, the method includes determining the application dependencies based on the identified network dependencies.

Container-aware application dependency identification

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Bengaluru, India

Dinesh Babu Thirukondan Gnaneswaran