Open Text Sa Ulc

Marc Rodriguez Sierra

Lalith Subramanian

Carles Bayes Martin

Sumitra Sahu

Jalendhar Baddam

Carles Bayés Martín

Systems and methods for content management wherein a client can submit requests to a first API which the forwards the requests to either an IMDB or a gateway to a distributed cluster-computing framework. Requests to the IMDB are serviced and responses from the IMDB are returned to the client. Requests that are forwarded to the gateway are first modified for the distributed cluster-computing framework, and are then parsed by the gateway and used to instantiate processors that generate corresponding requests to the distributed cluster-computing framework. Responsive data from the distributed cluster-computing framework is used to generate responses to the client requests that are forwarded to the first API which modifies them to appear as if they were generated by the IMDB. These modified responses are returned by the first API to the client.

Flexible and scalable artificial intelligence and analytics platform with flexible content storage and retrieval

A lightweight machine learning model (MLM) microservice is hosted in a cloud computing environment suitable for large-scale data processing. A client system can utilize the MLM service to run a MLM on a dataset in the cloud computing environment. The MLM can be already developed, trained, and tested using any appropriate ML libraries on the client side or the server side. However, no data schema is required to be provided from the client side. Further, neither the MLM nor the dataset needs to be persisted on the server side. When a request to run a MLM is received by the MLM service from a client system, a data schema is inferred from a dataset provided with the MLM. The MLM is run on the dataset utilizing the inferred data schema to generate a prediction which is then returned by the MLM service to the client system.

Growing community of inventors

Sant Just Desvern, Spain

Marc Rodriguez Sierra