ASML: Algorithm-Agnostic Architecture for Scalable Machine Learning
| Autor: | Dimitris K. Iakovidis, Dimitrios E. Diamantis |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Artificial intelligence
General Computer Science parallel processing Computer science Mission critical Context (language use) 02 engineering and technology Machine learning computer.software_genre Task (project management) distributed computing 020204 information systems 0202 electrical engineering electronic engineering information engineering General Materials Science Throughput (business) business.industry General Engineering machine vision medical services Modular design Scalability Systems architecture Task analysis 020201 artificial intelligence & image processing lcsh:Electrical engineering. Electronics. Nuclear engineering business computer Algorithm lcsh:TK1-9971 |
| Zdroj: | IEEE Access, Vol 9, Pp 51970-51982 (2021) |
| ISSN: | 2169-3536 |
| Popis: | Machine Learning (ML) applications are growing in an unprecedented scale. The development of easy-to-use machine-learning application frameworks has enabled the development of advanced artificial intelligence (AI) applications with only a few lines of self-explanatory code. As a result, ML-based AI is becoming approachable by mainstream developers and small businesses. However, the deployment of ML algorithms for remote high throughput ML task execution, involving complex data-processing pipelines can still be challenging, especially with respect to production ML use cases. To cope with this issue, in this paper we propose a novel system architecture that enables Algorithm-agnostic, Scalable ML (ASML) task execution for high throughput applications. It aims to provide an answer to the research question of how to design and implement an abstraction framework, suitable for the deployment of end-to-end ML pipelines in a generic and standard way. The proposed ASML architecture manages horizontal scaling, task scheduling, reporting, monitoring and execution of multi-client ML tasks using modular, extensible components that abstract the execution details of the underlying algorithms. Experiments in the context of obstacle detection and recognition, as well as in the context of abnormality detection in medical image streams, demonstrate its capacity for parallel, mission critical, task execution. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|