Adobe, Inc.

Shankar Srinivasan

Pari Sawant

Balaji Vasan Srinivasan

Moumita Sinha

Anandhavelu N

Kannan Iyer

Nirmal Mani

Seth Reilly

Jonathan Lancar

Systems and methods for developing enterprise machine learning (ML) models within a notebook application are described. The system may include a notebook application, a packaging service, and an online ML platform. The method may include initiating a runtime environment within the notebook application, creating a plurality of files based on a notebook recipe template, generating a prototype model within the data science notebook application by accessing the plurality of files through the runtime environment, generating a production recipe including the runtime environment and the plurality of files, and publishing the production recipe to the online ML platform.

Notebook interface for authoring enterprise machine learning models

Techniques and systems are provided for managing experience payloads associated with various users. A system obtains an experience payload and first biometric information of a user from a first computing device. The system generates a set of characteristic representations of the user using the first biometric information. The system associated the set of characteristic representations with the experience payload. In response to a request from a second computing device at a physical location to obtain the experience payload, the system evaluates biometric information from the request to determine whether it corresponds to the set of characteristic representations. If so, the system provides the experience payload to the second computing device to cause the second computing device to perform operations associated with the user at the physical location.

Biometric identification for payload association and retrieval

This disclosure relates to methods, non-transitory computer readable media, and systems that generate and visualize bias scores within segment-generation-user interfaces prior to executing proposed actions with regard to target segments. For example, the disclosed systems can generate a bias score indicating a measure of bias for a characteristic within a segment of users selected for a proposed action and visualize the bias score and corresponding characteristic in a segment-generation-user interface. In some implementations, the disclosed systems can further integrate detecting and visualizing bias as a bias score with selectable options for a segmentation-bias system to generate and modify segments of users to reduce detected bias.

Generating and visualizing bias scores representing bias in digital segments within segment-generation-user interfaces

Techniques are disclosed for automatically modeling and predicting moderator actions for online content. A model can be generated or updated based on the content received and the action or actions taken by the moderator in response to receiving the content. The model can be used to automatically predict which action, or combination of actions, are likely to be taken by the moderator when new content is received, and suggest those action(s) to the moderator. These suggestions can, among other things, simplify and speed up the decision-making process for the moderator.

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San Ramon, CA, United States of America

Shankar Srinivasan