Naeural AI OS -- Decentralized ubiquitous computing MLOps execution engine

• URL: http://arxiv.org/abs/2306.08708v2
• Date: Mon, 15 Apr 2024 17:24:56 GMT
• Title: Naeural AI OS -- Decentralized ubiquitous computing MLOps execution engine
• Authors: Beatrice Milik, Stefan Saraev, Cristian Bleotiu, Radu Lupaescu, Bogdan Hobeanu, Andrei Ionut Damian,
• Abstract summary: We present an innovative approach for low-code development and deployment of end-to-end AI cooperative application pipelines. We address infrastructure allocation, costs, and secure job distribution in a fully decentralized global cooperative community based on tokenized economics.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Over the past few years, ubiquitous, or pervasive computing has gained popularity as the primary approach for a wide range of applications, including enterprise-grade systems, consumer applications, and gaming systems. Ubiquitous computing refers to the integration of computing technologies into everyday objects and environments, creating a network of interconnected devices that can communicate with each other and with humans. By using ubiquitous computing technologies, communities can become more connected and efficient, with members able to communicate and collaborate more easily. This enabled interconnectedness and collaboration can lead to a more successful and sustainable community. The spread of ubiquitous computing, however, has emphasized the importance of automated learning and smart applications in general. Even though there have been significant strides in Artificial Intelligence and Deep Learning, large scale adoption has been hesitant due to mounting pressure on expensive and highly complex cloud numerical-compute infrastructures. Adopting, and even developing, practical machine learning systems can come with prohibitive costs, not only in terms of complex infrastructures but also of solid expertise in Data Science and Machine Learning. In this paper we present an innovative approach for low-code development and deployment of end-to-end AI cooperative application pipelines. We address infrastructure allocation, costs, and secure job distribution in a fully decentralized global cooperative community based on tokenized economics.

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