Tomás Pariente

Tomás Pariente

R&D Team Leader


The Applied AI team is dedicated to harness the power of Artificial Intelligence and High-Performance Computing to deliver tangible value to Eviden BDS customers across multiple industries. Our mission is to better capture, control and process Big Data to enable the development of advanced and trustworthy Artificial Intelligence state of the art technologies thanks to large scale computing solutions spanning the complete continuum and with a special focus on HPC.


The progress of hardware and software technologies during the last years has resulted in an exponential growth of the capabilities of AI and its adoption in a considerable number of new applications, including some with associated high risks. New regulations and standards are being promoted in order to ensure that the benefits of AI can be applied safely and trustworthily. Nevertheless, the provided recommendations require the design of good practices and the implementation of new building blocks for data governance and for the management of the life cycle of the models following the Machine Learning Operations (MLOps) paradigm.

Our team aims to contribute also to Eviden BDS HPC products since their synergy with AI has the potential to revolutionise multiple applications. The immense computational power demanded by AI’s data-driven algorithms are a perfect candidate for HPC systems that can be accessed on-premises or as a service. However, it is important to achieve an optimised usage of the available resources and the consequent energy consumption.

The goal of our team is to demonstrate, validate and extend the value of Eviden BDS AI and HPC products in specific use-cases proposed by our customers according to the most recent ethical, legal and technological guidelines.

Assets and products

The team is working on the following key assets and products:

  • Fast Machine Learning Engine (FastML): A Machine Learning toolbox for job orchestration on High Performance Computing (HPC) nodes, hiding the complexity of jobs management from the user perspective.
  • A set of microservices for optimizing HPC nodes resources, leveraging Artificial Intelligence to improve energy efficiency and decarbonization processes.
  • A Federated Learning (FL) framework to guarantee data privacy preservation while leveraging the collective intelligence of multiple clients.
  • End-to-end data management, data analytics and AIaaS platforms that cover the complete life cycle of Machine Learning models (MLOps) to guarantee trustworthiness, reproducibility, robustness and energy efficiency.