AI-Toolkit: A Microservices Architecture for Low-Code Decentralized Machine Intelligence

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI:10.1109/ICASSPW59220.2023.10193222

Vincenzo Lomonaco, Valerio De Caro, C. Gallicchio, Antonio Carta, Christos Sardianos, Iraklis Varlamis, K. Tserpes, M. Coppola, Mina Marmpena, S. Politi, E. Schoitsch, D. Bacciu

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Abstract

Artificial Intelligence and Machine Learning toolkits such as Scikit-learn, PyTorch and Tensorflow provide today a solid starting point for the rapid prototyping of R&D solutions. However, they can be hardly ported to heterogeneous decentralised hardware and real-world production environments. A common practice involves outsourcing deployment solutions to scalable cloud infrastructures such as Amazon SageMaker or Microsoft Azure. In this paper, we proposed an open-source microservices-based architecture for decentralised machine intelligence which aims at bringing R&D and deployment functionalities closer following a low-code approach. Such an approach would guarantee flexible integration of cutting-edge functionalities while preserving complete control over the deployed solutions at negligible costs and maintenance efforts.

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AI-Toolkit:用于低代码分散机器智能的微服务架构

人工智能和机器学习工具包，如Scikit-learn, PyTorch和Tensorflow，今天为研发解决方案的快速原型设计提供了坚实的起点。然而，它们很难移植到异构的分散硬件和真实的生产环境中。一个常见的做法是将部署解决方案外包给可扩展的云基础设施，如Amazon SageMaker或Microsoft Azure。在本文中，我们提出了一种基于开源微服务的分布式机器智能架构，旨在通过低代码方法使研发和部署功能更加紧密。这种方法将保证尖端功能的灵活集成，同时以可忽略不计的成本和维护工作保持对部署解决方案的完全控制。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)

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