Esax: Enhancing the Scalability of the Axyon platform.
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Axyon AI focuses on financial time series analysis using Machine Learning algorithms, partnering with financial institutions to enhance investment strategies performance and risk profiles. The pilot aims to work with EOSC DIH to demonstrate the use of EOSC infrastructure for company services.
Duration in months: 12
Problem Description
The key result of the ESAX project was bringing the computational scalability of the Axyon Platform to a new level, almost quadrupling the previous peak of parallely executed jobs, with no issue in terms of system management load or network utilization.
Goals
New services
Challenges
The main objective of the pilot is to work with EOSC DIH as a proof of concept of using the EOSC infrastructure and competences to enhance the TRL of the company services.
Innovation results
The ESAX project significantly improved the computational scalability of the Axyon Platform, enabling parallel job execution and optimizing large deep neural network models on multi-GPU multi-node HPC clusters, reducing execution times and enabling next-generation computing capabilities.
Business impact
The Axyon Platform scalability has been significantly enhanced, allowing for the execution of more parallel jobs and the training of AI models with larger datasets, potentially including a wider range of financial assets with much higher granularity level and different explanatory variables.