Developments on the “Machine Learning as a Service for High Energy Physics” Framework and Related Cloud Native Solution

Machine Learning (ML) techniques have been successfully used in many areas of High Energy Physics (HEP) and will play a significant role in the success of upcoming High-Luminosity Large Hadron Collider (HL-LHC) program at CERN. An unprecedented amount of data at the exascale will be collected by LHC experiments in the next decade, and this effort will require novel approaches to train and use ML models. The work presented in this paper is focused on the developments of a ML as a Service (MLaaS) solution for HEP, aiming to provide a cloud service that allows HEP users to run ML pipelines via HTTPs calls. These pipelines are executed by using MLaaS4HEP framework, which allows reading data, processing data, and training ML models directly using ROOT files of arbitrary size from local or distributed data sources. In particular, new features implemented on the framework will be presented as well as updates on the architecture of an existing prototype of the MLaaS4HEP cloud service will be provided. This solution includes two OAuth2 proxy servers as authentication/authorization layer, a MLaaS4HEP server, an XRootD proxy server for enabling access to remote ROOT data, and the TensorFlow as a Service (TFaaS) service in charge of the inference phase.