Amazon Technologies, Inc.

Sunny Dasgupta

Sri Kaushik Pavani

Shriram Venkataramana

Sabya Sachi

Himanshu Prafulla Shringarpure

Shikhar Gupta

Rajul Mittal

Niyaz Puzhikkunnath

Mathew Philip

Divya Varshney

Shubhangam Agrawal

Fnu Najih

Suryansh Purwar

An interactive interpretation session with respect to a first version of a machine learning model is initiated. In the session, indications of factors contributing to a prediction decision are provided, as well indications of candidate model enhancement actions. In response to received input, an enhancement action is implemented to obtain a second version of the model. The second version of the model is stored.

Artificial intelligence system providing interactive model interpretation and enhancement tools

Computer systems and associated methods are disclosed to implement a model development environment (MDE) that allows a team of users to perform iterative model experiments to develop machine learning (ML) media models. In embodiments, the MDE implements a media data management interface that allows users to annotate and manage training data for models. In embodiments, the MDE implements a model experimentation interface that allows users to configure and run model experiments, which include a training run and a test run of a model. In embodiments, the MDE implements a model diagnosis interface that displays the model's performance metrics and allows users to visually inspect media samples that were used during the model experiment to determine corrective actions to improve model performance for later iterations of experiments. In embodiments, the MDE allows different types of users to collaborate on a series of model experiments to build an optimal media model.

User interface for visual diagnosis of image misclassification by machine learning

Fast annotation of samples for machine learning model development

System for visually diagnosing machine learning models

Computer systems and associated methods are disclosed to implement a collaborative dataset management system (CDMS) for machine learning (ML) data. In embodiments, CDMS allows many users to create, review, and collaboratively evolve ML datasets. In embodiments, dataset owners may make their datasets available to other users on CDMS for a fee and under specified licensing conditions. CDMS users can search for other users' datasets on the system to use in their own ML tasks. CDMS users may also create child datasets from existing datasets on the system. Parent and child datasets may be linked so that changes to one dataset are provided to the other via merge requests. A dataset owner may use CDMS to review an incoming merge request using one or more audit jobs before approving the request. In this manner, CDMS provides a shared repository and collaboration system for managing high-quality datasets to power machine learning processes.

Collaborative dataset management system for machine learning data

Development environment for machine learning media models

Growing community of inventors

Redmond, WA, United States of America

Sunny Dasgupta