Machine learning (ML) infrastructure techniques

US11663523B2 · kind B2 · utility

Assignee

• Oracle International Corporation · US

Inventors

• Alberto Polleri · London, GB
• Sergio Aldea Lopez · London, GB
• Marc Michiel Bron · London, GB
• Dan David Golding · London, GB
• Alexander Ioannides · London, GB
• Maria del Rosario Mestre · London, GB
• Hugo Alexandre Pereira Monteiro · London, GB
• Oleg Gennadievich Shevelev · London, GB
• Larissa Cristina Dos Santos Romualdo Suzuki · London, GB
• Xiaoxue Zhao · London, GB
• Matthew Charles Rowe · Milton Keynes, GB

Key dates

• Filing date: Jun 4, 2020
• Grant date: May 30, 2023
• Expiry date: May 24, 2041

Classification

• Technology area (CPC G): Physics
• CPC primary: G06N20/20
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

The present disclosure relates to systems and methods for a machine learning platform that generates a library of components to generate machine learning models and machine learning applications. The machine learning infrastructure system allows a user (i.e., a data scientist) to generate machine learning applications without having detailed knowledge of the cloud-based network infrastructure or knowledge of how to generate code for building the model. The machine learning platform can analyze the identified data and the user provided desired prediction and performance characteristics to select one or more library components and associated API to generate a machine learning application. The machine learning can monitor and evaluate the outputs of the machine learning model to allow for feedbacks and adjustments to the model. The machine learning application can be trained, tested, and compiled for export as stand-alone executable code.