Paypal, Inc.

Yuehao Wu

Junhua Zhao

Chun Kiat Ho

Lin Song

Srinivasan Manoharan

Xiaohan Yun

Vinesh Chirakkil

Harish Nalagandla

Meng Zang

Venkata Ramana Nadimpalli

Vahini Pobbathi

Ming Zhu

Jinwoo Lee

Xiaoying Han

Premila Viswanathan

Fei Li

Jun Yang

Xin Xu

Xin Li

Pengshan Zhang

Kai Xie

Zhenyin Yang

Yunfeng Li

Quin Zuo

Ke Zheng

Wen Lu

Rajesh Virupaksha Munavalli

Adrian Oliver Suciu

Chinmayee Rathi

A system performs operations that include receiving, via first computing environment, a request to process text data using a first natural language processing (NLP) model. The operations further include accessing configuration data associated with the NLP model, where the configuration data generated using a domain specific language that supports a plurality of preprocessing modules in a plurality of programming languages. The operations also include selecting, based on the configuration data, one or more preprocessing modules of the plurality of preprocessing modules, generating, based on the configuration data, a preprocessing pipeline using the one or more preprocessing modules, and generating preprocessed text data by inputting the text data into the preprocessing pipeline. The preprocessed text data is provided to the first NLP model.

Framework for managing natural language processing tools

Methods and systems are presented for providing a container orchestration framework for facilitating development and deployment of software applications across different operating environments within an enterprise system. Upon receiving a service request for processing a set of data is received, the container orchestration framework determines one or more machines that store the set of data. Instead of processing the set of data remotely, the container orchestration framework deploys a container that encapsulates an application on the one or more machines. Each application instance running on the one or more machines are executed to process a corresponding subset of data stored on the machine locally. The container orchestration framework obtains the output data from executing the applications on each of the one or more machines, and present the output data as a response to the service request.

Container orchestration framework

A computer system receives a set of user-defined rules are that useable by a computer service to automate a decision flow. The computer system generates a graph model from the user-defined rules. From the graph model, the computer system determines an input dependency model that is indicative of a set of inputs referred to in the graph model. The input dependency model is useable by an orchestrator to coordinate accesses to the one or more data stores in which the set of inputs is stored. The computer system receives the set of inputs and determines one or more automated decisions by applying the set of inputs to the graph model.

Automated decision platform

Techniques are disclosed relating to transitioning between data stores using a data migration server. In some embodiments, the data migration server may be used to access data stored on a preexisting data store to service requests from a plurality of services. A dual-write operation mode for the data migration server may then be enabled such that, in response to a given write request, the data migration server writes a given data entry to both the preexisting data store and a replacement data store. Further, a dual-read operation mode may be enabled such that, in response to a given read request, the data migration server reads a corresponding data entry from both the preexisting and replacement data stores. Configuration settings for the data migration server may then be adjusted to designate the replacement data store as the primary data store to service requests from the services.

Data store transition using a data migration server

A first graph that includes a plurality of containers is accessed. The containers each contain one or more rules that each have corresponding computer code. The containers are configured for sequential execution by a rule engine. The computer code corresponding to the one or more rules in each of the containers is electronically scanned. Based on the electronic scan, an interdependency among the rules is determined. Based on the determined interdependency, a second graph is generated. The second graph includes all of the rules of the containers, but not the containers themselves. At least some of the rules are configured for parallel execution by the rule engine.

Rule engine optimization via parallel execution

Techniques are disclosed relating to accessing, by an evaluation computer system, transaction details from a subset of current transactions being processed by a production version of a transaction processing service that is implemented on a production computer system. The evaluation computer system may perform, in real-time, tests on a particular product using the transaction details. The evaluation computer system may then store output from the tests that are performed using the transaction details.

Product evaluation using transaction details from a production system

A method may include receiving instructions to process a first application in response to a user request. The method also includes determining whether to store the first application in a first processing queue or a second processing queue based on a comparison between a CPU processing cost associated with the first application and a GPU processing cost associated with the first application. Further, the method includes grouping a first set of applications stored in the first processing queue according to CPU grouping criteria and grouping a second set of applications stored in the second processing queue according to GPU batching criteria. The method also includes causing a CPU to process the grouped first set of applications and a plurality of GPUs to process the grouped second set of applications.

Scheduling applications in CPU and GPU hybrid environments

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Cupertino, CA, United States of America

Yuehao Wu