Machine Learning as a Service for HEP

by   Valentin Kuznetsov, et al.

Machine Learning (ML) will play significant role in success of the upcoming High-Luminosity LHC (HL-LHC) program at CERN. The unprecedented amount of data at the Exa-Byte scale to be collected by the CERN experiments in next decade will require a novel approaches to train and use ML models. In this paper we discuss Machine Learning as a Service (MLaaS) model which is capable to read HEP data in their native ROOT data format, rely on the World-Wide LHC Grid (WLCG) infrastructure for remote data access, and serve a pre-trained model via HTTP protocol. Such modular design opens up a possibility to train data at large scale by reading ROOT files from remote storages, avoiding data-transformation to flatten data formats currently used by ML frameworks, and easily access pre-trained ML models in existing infrastructure and applications.


page 1

page 2

page 3

page 4


Chiron: Privacy-preserving Machine Learning as a Service

Major cloud operators offer machine learning (ML) as a service, enabling...

Managing Data Lineage of O G Machine Learning Models: The Sweet Spot for Shale Use Case

Machine Learning (ML) has increased its role, becoming essential in seve...

Towards Demystifying Serverless Machine Learning Training

The appeal of serverless (FaaS) has triggered a growing interest on how ...

Assuring the Machine Learning Lifecycle: Desiderata, Methods, and Challenges

Machine learning has evolved into an enabling technology for a wide rang...

Machine learning enabling high-throughput and remote operations at large-scale user facilities

Imaging, scattering, and spectroscopy are fundamental in understanding a...

Metadata Representations for Queryable ML Model Zoos

Machine learning (ML) practitioners and organizations are building model...

Probabilistic thermal stability prediction through sparsity promoting transformer representation

Pre-trained protein language models have demonstrated significant applic...

Please sign up or login with your details

Forgot password? Click here to reset