Prototype of a cloud native solution of Machine Learning as Service for HEP

To favor the usage of Machine Learning (ML) techniques in High-Energy Physics (HEP) analyses it would be useful to have a service allowing to perform the entire ML pipeline (in terms of reading the data, training a ML model, and serving predictions) directly using ROOT files of arbitrary size from local or remote distributed data sources. The MLaaS4HEP framework aims to provide such kind of solution. It was successfully validated with a CMS physics use case which gave important feedback about the needs of analysts. For instance, we introduced the possibility for the user to provide pre-processing operations, such as defining new branches and applying cuts. To provide a real service for the user and to integrate it into the INFN Cloud, we started working on MLaaS4HEP cloudification. This would allow to use cloud resources and to work in a distributed environment. In this work, we provide updates on this topic, and in particular, we discuss our first working prototype of the service. It includes an OAuth2 proxy server 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. With this architecture the user is able to submit ML pipelines, after being authenticated and authorized, using local or remote ROOT files simply using HTTP calls