Recent AI research has significantly reduced the barriers to apply AI, but
the process of setting up the necessary tools and frameworks can still be a
challenge. While AI-as-a-Service platforms have emerged to simplify the
training and deployment of AI models, they still fall short of achieving true
democratization of AI. In this paper, we aim to address this gap by comparing
several popular AI-as-a-Service platforms and identifying the key requirements
for a platform that can achieve true democratization of AI. Our analysis
highlights the need for self-hosting options, high scalability, and openness.
To address these requirements, we propose our approach: the "Open Space for
Machine Learning" platform. Our platform is built on cutting-edge technologies
such as Kubernetes, Kubeflow Pipelines, and Ludwig, enabling us to overcome the
challenges of democratizing AI. We argue that our approach is more
comprehensive and effective in meeting the requirements of democratizing AI
than existing AI-as-a-Service platforms.

通过比较几个热门的 AI 即服务平台并确定实现人工智能真正民主化的关键要求，本文提出了一种新的方法，即 “开放空间机器学习” 平台，它基于 Kubernetes、Kubeflow Pipelines 和 Ludwig 等先进技术，解决了民主化 AI 的挑战，并比现有的 AI 即服务平台更全面和有效。

迈向普及人工智能: AI 即服务平台与机器学习开放空间的比较分析

Towards Democratizing AI: A Comparative Analysis of AI as a Service  Platforms and the Open Space for Machine Learning Approach

AI requires heavy amounts of storage and compute with assets that are
commonly stored in AI Hubs. AI Hubs have contributed significantly to the
democratization of AI. However, existing implementations are associated with
certain benefits and limitations that stem from the underlying infrastructure
and governance systems with which they are built. These limitations include
high costs, lack of monetization and reward, lack of control and difficulty of
reproducibility. In the current work, we explore the potential of decentralized
technologies - such as Web3 wallets, peer-to-peer marketplaces, storage and
compute, and DAOs - to address some of these issues. We suggest that these
infrastructural components can be used in combination in the design and
construction of decentralized AI Hubs.

本文旨在探讨去中心化技术，如 Web3 钱包、点对点市场、存储和计算，以及 DAO 相结合，可用于设计和构建去中心化 AI Hub。