AI Platform for INFN Scientific Use Cases
Creators: name: Gattari, Mauro
Publisher: name: INFN Open Access Repository
Year: 2024

description: Talk given at the International Symposium on Grids &amp; Clouds (ISGC), Taipei 24-29/03/2024

<strong>Abstract</strong>

Researchers at INFN (National Institute for Nuclear Physics) face challenges from basic to hard science<br>
use cases (e.g., big-data latest generation experiments) in many areas: HEP (High Energy Physics),<br>
Astrophysics, Quantum Computing, Genomics, etc.<br>
Machine Learning (ML) adoption is ubiquitous in these areas, requiring researchers to solve problems<br>
related to the specificity of applications (e.g., tailored models and intricate domain knowledge), but also<br>
requiring solving general infrastructure-level and ML-workflow related problems.<br>
As the demand for ML solutions continues to rise across the diverse research domains, there exists a<br>
critical need for an innovative approach to accelerate ML adoption.<br>
In this regard we propose an AI platform designed as an application-agnostic MLaaS (Machine Learning<br>
as a Service) solution, which provides a paradigm shift by offering a flexible and generalized<br>
infrastructure that decouples the ML development process from specific use cases.<br>
The AI platform is implemented as a software layer on top of our cloud platform, the INFN Cloud, which<br>
offers composable, scalable, and open-source solutions on a dedicated geographically distributed<br>
infrastructure. The INFN Cloud core mission is to facilitate resource sharing and enhance accessibility for<br>
INFN users, encompassing a wide range of resources, including GPUs and storage.<br>
The AI platform leverages INFN Cloud resources and principles, gathering and orchestrating technologies<br>
to support end-to-end scalable ML solutions: Kubernetes, Kubeflow, KServe, KNative, Kueue, Horovod,<br>
etc., ensuring support for many ML frameworks: TensorFlow, PyTorch, Apache MXNet, XGBoost, etc.<br>
This contribution will describe the platform’s design and principles, as well as some selected use cases<br>
from NLP and HEP domains that benefitted from the “aaS” approach.<br>
The platform's agnostic nature extends beyond model compatibility to address the practical challenges<br>
associated with deploying ML solutions in real hard science scenarios: streaming services, exabyte-scale<br>
storage solutions, high-bandwidth networking, support for native HEP data (e.g., CERN ROOT data<br>
format), etc.<br>
Furthermore, the AI platform promotes transfer learning and model reuse, to accelerate the ML<br>
development lifecycle. Developers can leverage pre-trained models and share knowledge across<br>
different applications, reducing the time and resources required for training new models from scratch.<br>
This collaborative aspect not only enhances efficiency but also promotes a collective learning<br>
environment within the research community.<br>
In conclusion, the application-agnostic AI platform serves as a unified ecosystem where developers, data<br>
scientists, and domain experts can collaborate seamlessly. By providing a standardized framework for<br>
ML model development, training, and deployment, the platform eliminates the need for extensive<br>
domain expertise in every application area. This democratization of ML empowers a broader audience<br>
to leverage the benefits of machine learning, breaking down barriers and fostering innovation across<br>
diverse research domains.