  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.
