Microsoft Technology Licensing, LLC

Apple Inc.

Zachary A Nation

Srikrishna Sridhar

Gaurav Kapoor

Alexander Barraclough Brown

Michael R Siracusa

Christopher M Hanson

Vrushali Mundhe

Elizabeth Ottens

Michael C Fanning

Arye Gittelman

Frederico A Mameri

Aditi Mandal

Ajey Pankaj Shah

Michael Brandon Silverstein

Emre Bogazliyanlioglu

Yubo Jia

John William Strick

Alexander S Spitsyn

Charles Reeves Little, Ii

Jonathan Fred Kern

Shaofeng Zhu

Kanit Wongsuphasawat

Frank Feng-Chun Chiang

Tim McConnell

Cindy Liao

Aaron B Franklin

Naga Rama Abhishek Pratapa

Alan T Norbauer

Jeff Crowley

The subject technology transforms a machine learning model into a transformed machine learning model in accordance with a particular model specification when the machine learning model does not conform to the particular model specification, the particular model specification being compatible with an integrated development environment (IDE). The subject technology generates a code interface and code for the transformed machine learning model, the code interface including code statements in the object oriented programming language, the code statements corresponding to an object representing the transformed machine learning model. Further, the subject technology provides the generated code interface and the code for display in the IDE, the IDE enabling modifying of the generated code interface and the code.

Integration of learning models into a software development system

The subject technology receives information associated with a machine learning model. The subject technology determines a set of metrics based at least in part on the information associated with the machine learning model, where the set of metrics corresponds to respective indicators of performance of the machine learning model based on input data from a data set, the set of metrics further including a number of errors produced by the machine learning model when applied to the input data from the data set. Further, the subject technology displays a user interface based at least in part on the set of metrics, where the user interface includes a set of graphical elements, and the set of graphical elements further includes representations of the set of metrics, and representations of the input data from the data set utilized by the machine learning model.

Data visualization machine learning model performance

The subject technology provides for generating machine learning (ML) model code from a ML document file, the ML document file being in a first data format, the ML document file being converted to code in an object oriented programming language different than the first data format. The subject technology further provides for receiving additional code that calls a function provided by the ML model code. The subject technology compiles the ML model code and the additional code, the compiled ML model code including object code corresponding to the compiled ML model code and the compiled additional code including object code corresponding to the additional code. The subject technology generates a package including the compiled ML model code and the compiled additional code. Further, the subject technology sends the package to a runtime environment on a target device for execution.

The subject technology provides for determining that a machine learning model in a first format includes sufficient data to conform to a particular model specification in a second format, the second format corresponding to an object oriented programming language. The subject technology transforms the machine learning model into a transformed machine learning model that is compatible with the particular model specification. The subject technology generates a code interface and code for the transformed machine learning model, the code interface including code statements in the object oriented programming language, the code statements corresponding to an object representing the transformed machine learning model. Further, the subject technology provides the generated code interface and the code for display in an integrated development environment (IDE), the IDE enabling modifying of the generated code interface and the code.

A stub can be loaded into a first browser environment of a browser application on a client machine, with the stub being loaded from a domain. The stub can execute to load an online application test into the first browser environment. Additionally, the test can execute in the first browser environment to conduct the test on an online application. For example, the test may be conducted from a second browser environment of the browser on the client machine. Performing the test can include loading one or more digital pages from the application into the second browser environment.

Online application testing across browser environments

Concepts and technologies are described herein for managing script file dependencies and load times. In accordance with the concepts and technologies disclosed herein, a server computer stores pages and provides the pages to a client device. The pages can reference and/or rely upon one or more script files during execution and/or rendering. The server computer can determine script file dependencies and load levels associated with the script files. The server computer can generate data such as a directed graph and/or a table that can indicate script file dependencies and script file load levels. The data can be provided to a client device or other entity for use during loading, rendering, and/or execution of the pages.

Managing script file dependencies and load times

A cross-browser dialog platform is provided for supporting hosted web pages in a dialog, such that a web page may be used inside or outside the dialog. Select elements on the hosted page may be hidden when in a dialog, different behavior may be specified for hyperlinks inside or outside the dialog, and a proper size of the dialog may be inferred from its contents without designer input.

Cross-browser web dialog platform

Transforming dynamic code. The method includes obtaining one or more first data structures defining constructs in a body of dynamic language source code. From the one or more first data structures, identifier information is extracted for one or more of the defined constructs. Knowledge about the constructs is augmented. Using the identifier information and augmented knowledge, metadata is generated about the body of the dynamic language source code. The generated metadata is represented as a symbol table. Using the symbol table, the body of dynamic language source code is transformed.

Transforming dynamic source code based on semantic analysis

Analyzing dynamic source code. A method includes accessing a specific metadata format data structure. The data structure was created by obtaining one or more first data structures defining constructs in a body of dynamic language source code. From the one or more first data structures, identifier information is extracted for one or more of the defined constructs. Knowledge about the constructs is augmented. The metadata format data structure is parsed to compute metrics about the metadata format data structure. The metrics about the metadata format data structure are provided to a user.

Static semantic analysis of dynamic languages

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Seattle, WA, United States of America

Zachary A Nation