MLaaS4HEP: Machine Learning as a Service for HEP

TL;DR

This paper introduces MLaaS4HEP, a modular machine learning pipeline tailored for high-energy physics, enabling scalable data processing, model training, and inference for large-scale LHC data analysis.

Contribution

It presents a novel, multi-layer ML as a Service framework for HEP that integrates ROOT data handling, distributed training, and model serving, facilitating large-scale analysis.

Findings

Demonstrated effective training of ML models using distributed ROOT files.

Showed the MLaaS4HEP architecture's performance in a CMS $t\bar{t}$ Higgs analysis.

Compared ML-based methods with traditional analysis techniques.

Abstract

Machine Learning (ML) will play a significant role in the success of the upcoming High-Luminosity LHC (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. In this paper, we discuss a Machine Learning as a Service pipeline for HEP (MLaaS4HEP) which provides three independent layers: a data streaming layer to read High-Energy Physics (HEP) data in their native ROOT data format; a data training layer to train ML models using distributed ROOT files; a data inference layer to serve predictions using pre-trained ML models via HTTP protocol. Such modular design opens up the possibility to train data at large scale by reading ROOT files from remote storage facilities, e.g. World-Wide LHC Computing Grid (WLCG) infrastructure, and feed the data to the user's favorite ML framework. The inference layer implemented as TensorFlow as a Service (TFaaS) may provide an easy access to pre-trained ML models in existing infrastructure and applications inside or outside of the HEP domain. In particular, we demonstrate the usage of the MLaaS4HEP architecture for a physics use-case, namely the $t\bar{t}$ Higgs analysis in CMS originally performed using custom made Ntuples. We provide details on the training of the ML model using distributed ROOT files, discuss the performance of the MLaaS and TFaaS approaches for the selected physics analysis, and compare the results with traditional methods.