Ratio1 meta-OS - decentralized MLOps and beyond

Ubiquitous computing continues to permeate modern systems, from enterprise platforms to consumer apps and gaming ecosystems, by embedding networked devices seamlessly into everyday environments. This integration fosters enhanced connectivity and collaboration, enabling more efficient and sustainable communities. However, the universal adoption of Artificial Intelligence and Deep Learning within these distributed infrastructures has been hindered by expensive cloud deployments, complex DevOps processes, and limited expertise in data science. Building on our original work in the area of decentralized ubiquitous computing this paper presents the scope and capabilities of our decentralized Ratio1^† AI ecosystem.In this paper we argue that, as a meta-operating system, Ratio1 addresses the aforementioned challenges head-on through multiple pillars such as tokenization, low-code development and deployment frameworks - all automating large-scale machine learning workflows, emphasizing cooperative pipelines and decentralized resource utilization. Through a token-based economic model, participants can securely share compute resources and collaborate without reliance on monolithic cloud providers. Building upon the principles introduced in our initial research, we demonstrate how Ratio1 AI OS improves accessibility, scalability, and cost-effectiveness for real-world AI applications—ultimately offering a holistic, decentralized MLOps solution that redefines the boundaries of ubiquitous computing.