Amazon Technologies, Inc.

Facebook, Inc.

Ravneet Singh Sidhu

Lionel Bitoun

Ramanathan Padinjarel Somanathan

Manjunath Tumkur Maheshchandra

Sachin Suresh Bhat

LiJing Chen

Harshaneel Harshal Gokhale

Sourabh Shrivastav

Jaikit Dungarshi Savla

Rutvik Gopalkrishna Hora

Manoj Kumar Dhanger

Daniel Wu

Nitin Saini

Adam Tessier

Jeet Nishit Mehta

Jaden Wright

Ruonan Zhang

A data storage interface layer provides access management and transformation of data stored in various backend storage clusters. The data storage interface can serve as a point of access for data accessors to access stored data via a consistent data access protocol, even when a data storage cluster on which requested data is stored may use a different protocol. The data storage interface can also provide in-line transformation of requested data and/or control of access to requested data.

Data storage interface layer with access and transformation management

A data storage interface provides access to data storage clusters that may not otherwise be accessible to data accessors. The data storage interface can serve as a point of access for data accessors to access stored data via a consistent data access protocol, even when a data storage cluster on which requested data is stored may use a different protocol. The data storage interface can also provide access across network boundaries, such as those between different private cloud computing systems (e.g., virtual private clouds or 'VPCs').

Data storage interface for protocol-agnostic storage services

An online system detects imposter pages based on machine learning techniques. The online system maintains a plurality of authenticated pages and a plurality of unauthenticated pages, each of which is associated with a name and an image. From the plurality of unauthenticated pages, the online system filters out one or more unauthenticated pages that are associated with names of authenticated pages to obtain a group of candidate pages. Further, the online system pairs each candidate page up with an authenticated page. The candidate page has a name and/or image similar to the authenticated page. The online system inputs the candidate page and the authenticated page into a trained model. The trained model outputs an imposter score indicating a likelihood that the candidate page is an imposter page. The online system takes actions on the candidate page based on the imposter score.

Using machine learning techniques to detect imposter pages in an online system

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Mountain View, CA, United States of America

Ravneet Singh Sidhu