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Recent AI research has significantly reduced the barriers to apply AI, butthe process of setting up the necessary tools and frameworks can still be achallenge. While AI-as-a-Service platforms have emerged to simplify thetraining and deployment of AI models, they still fall short of achieving truedemocratization of AI. In this paper, we aim to address this gap by comparingseveral popular AI-as-a-Service platforms and identifying the key requirementsfor a platform that can achieve true democratization of AI. Our analysishighlights the need for self-hosting options, high scalability, and openness.To address these requirements, we propose our approach: the "Open Space forMachine Learning" platform. Our platform is built on cutting-edge technologiessuch as Kubernetes, Kubeflow Pipelines, and Ludwig, enabling us to overcome thechallenges of democratizing AI. We argue that our approach is morecomprehensive and effective in meeting the requirements of democratizing AIthan existing AI-as-a-Service platforms.

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