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 ttˉ 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.Comment: 16 pages, 10 figures, 2 tables, submitted to Computing and Software
for Big Science. arXiv admin note: text overlap with arXiv:1811.0449
