Towards Democratizing AI: A Comparative Analysis of AI as a Service Platforms and the Open Space for Machine Learning Approach
This work addresses the problem of making AI more accessible for users by proposing a new platform, but it appears incremental as it builds on existing technologies without demonstrating broad SOTA impact.
The paper tackles the challenge of democratizing AI by comparing AI-as-a-Service platforms and proposing the 'Open Space for Machine Learning' platform, which uses technologies like Kubernetes and Kubeflow to address gaps in self-hosting, scalability, and openness.
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.