Machine Learning (ML) techniques have been successfully used in many areas of High Energy Physics (HEP). Nevertheless, the development of a ML project and its implementation for production use is a highly time-consuming task and requires specific skills. Generally, HEP analysts do not have the skills in data science to tackle such challenges on their own. Furthermore, complicating this scenario is the existing gap between HEP and ML communities which is partly due to the fact that HEP data is stored in ROOT data format, which is mostly unknown outside of the HEP community.
Here we present a ML as a Service (MLaaS) solution for HEP [1,2], aiming to provide a cloud service that allows HEP users to run ML pipelines via HTTP calls. These pipelines are executed by using the MLaaS4HEP framework [3], which allows reading data, processing data, and training ML models directly using ROOT files of arbitrary size from local or distributed data sources. Such a solution would help to bridge the gap between ML and HEP communities, by providing HEP users non-expert in ML with a tool that allows them to apply ML techniques in their analyses in a streamlined manner.
Over the years the MLaaS4HEP framework has been developed, validated and tested and new features have been added. A first MLaaS solution has been developed by automatizing the deployment of a platform equipped with the MLaaS4HEP framework. Then, a service with APIs [4] has been developed, so that a user after being authenticated and authorized can submit MLaaS4HEP workflows producing trained ML models ready for the inference phase. A working prototype of this service is currently running on a VM of INFN-Cloud and is compliant to be added to the INFN Cloud portfolio of services.
[1] https://link.springer.com/article/10.1007/s41781-021-00061-3
[2] https://doi.org/10.22323/1.414.0968
[3] https://github.com/lgiommi/MLaaS4HEP
[4] https://github.com/lgiommi/MLaaS4HEP_server
