Towards Democratizing AI: A Comparative Analysis of AI as a Service
  Platforms and the Open Space for Machine Learning Approach
Dennis Rall Bernhard Bauer Thomas Fraunholz
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.