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。