Microsoft Technology Licensing, LLC

Dell Products Inc.

Vmware, Inc.

Kiran Rama

Laurent Boué

Vijay Srinivas Agneeswaran

Yasmin Bokobza

Ranjani Subramaniam

Sagar Balan

Stephen Harris

Ravi Prasad Kondapalli

Sayan Putatunda

Ricky Ho

Francis Chow

Swarnim Narayan

Chepuri Shri Krishna

Dayananda Ubrangala

A method, computer program product, and computing system for processing telemetry data associated with a target abbreviated term and a plurality of expanded terms. A plurality of probabilistic tuples are generated from the telemetry data associated with the target abbreviated term. A neural network is trained using the plurality of probabilistic tuples by modifying, with each training epoch, input data to the neural network by coupling a target abbreviated term with an expanded term associated with the target abbreviated term that is randomly selected from the plurality of expanded terms.

System and method for semantic searching of abbreviated terms using telemetry data-trained neural networks

A computing system encodes a next graph based on modified source code files recorded by the next code commit event. The computing system inputs the next graph to a graph machine learning model, the graph machine learning model being trained by graphs representing modified source code files and software test results corresponding to multiple code commit events occurring prior to the next code commit event in the sequence of code commit events. The computing system determines an order of test cases of the next code commit event using the graph machine learning model in an inference mode. The computing system executes the test cases according to the order during the software development build process corresponding to the next code commit event.

Test case prioritization

Example aspects include techniques for anomaly detection via sparse judgmental samples. These techniques may include generating, via lexical analysis, a plurality of tokens from a textual representation of a machine learning (ML) model and generating, via a parser, based on the plurality of tokens, an abstract syntax tree (AST) corresponding to the ML model. In addition, the techniques may include identifying a data dependency of the ML model based on an AST node within the AST, the AST node corresponding to a data source and the data dependency indicating the ML model depends on the data source. Further, the techniques may include detecting a potential issue associated with the data source, and transmitting, based on the data dependency, an alert notification in response to the potential issue.

Method and system to extract data dependencies for machine learning models

Systems and methods are described for identifying and resolving performance issues of automated components. The automated components are segmented into groups by applying a K-means clustering algorithm thereto based on segmentation feature values respectively associated therewith, wherein an initial set of centroids for the K-means clustering algorithm is selected by applying a set of context rules to the automated components. Then, for each group, a performance ranking is generated based at least on a set of performance feature values associated with each of the automated components in the group and a feature importance value for each of the performance features. The feature importance values are determined by training a machine learning based classification model to classify automated components into each of the groups, wherein the training is performed based on the respective performance feature values of the automated components and the respective groups to which they were assigned.

System and method for identifying and resolving performance issues of automated components

Methods, systems, apparatuses, and computer-readable storage mediums are descried for identifying a similarity between queries. An intermediate representation generator receives a set of queries from a repository, each query in the set of queries having generated a corresponding set of data stored in a data store. An intermediate representation is generated for each query, where the intermediate representation is characterized by a feature associated with text specified in the query. A similarity determiner determines similarity scores between pairs of intermediate representations. A pair of intermediate representations with a similarity score above a threshold is identified. An indication is generated that sets of data corresponding to queries corresponding to the intermediate representations are overlapping.

Approximate query equivalence for feature stores in machine learning operations products

Described are examples for information discovery using domain-specific term matching, including obtaining, for a search session related to a domain, a set of search strings used for searching the domain for information discovery during the search session, identifying, for the search session, a string of the set of search strings as a search string hit and the remaining strings in the set of search strings as search string misses, and correlating, into a set of domain-specific search string misses, the remaining strings in the set of search strings with additional remaining search strings from one or more other search session related to the domain that have the same string identified as the search string hit.

Techniques for associating domain-specific terms for information discovery

A system and method for recommending demand-supply agent pairs for transactions uses a deep neural network on data of demand agents to produce a demand agent vector, which is used to select supply agents based on their likelihood of future transaction and to find k nearest neighbor demand agents for each of the demand agents. The candidate supply agents and the k nearest neighbor demand agents are then combined to produce candidate demand-supply agent pairs, which are used to find recommended demand-supply agent pairs by applying modeling using machine learning.

System and method for recommending demand-supply agent combination pairs for transactions using machine learning

A method for comparing sales performance of two web pages includes receiving usage data associated with each web page. The usage data includes next-click visit mix information and sales consummation information. A metric is determined based on a visit mix associated with a link category at a second web page, and further based on a difference between a consummation ratio of the link category at the second web page and a consummation ratio of the link category at a first web page.

Method for comparing sales performance of web sites and a system therefor

A system, method, and computer-readable medium are disclosed for managing the display of product module descriptors within a user interface. Product module data, along with historical visit and product module purchase data, is processed to generate a Config Score numeric value, which in turn is processed to generate a ConfigRank numeric value for each of a plurality of product modules. In turn, the ConfigRank numeric values are then used to determine the placement of each product module's associated product module descriptor within a user interface window.

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Bengaluru, India

Kiran Rama