Artificial Intelligence as a Service (AIaaS)

Contributor(s): Matthew Haughn

Artificial Intelligence as a Service (AIaaS) is the third party offering of artificial intelligence (AI) outsourcing. AI as a service allows individuals and companies to experiment with AI for various purposes without large initial investment and with lower risk. Experimentation can allow the sampling of multiple public cloud platforms to test different machine learning algorithms.

Different AI provider platforms offer a number of styles of machine learning and AI. These variations can be more or less suited to an organization's AI needs since organizations need to evaluate features and pricing to see what works for them. Cloud AI service providers can offer the specialized hardware needed for some AI tasks, like GPU based processing for intensive workloads. Purchasing the required hardware and software to start on-premise cloud AI is costly. Coupled with staffing and maintenance costs, as well as hardware changes for different tasks, makes AIaaS cost prohibitive to many organizations.

AI cloud offerings including Amazon Machine Learning, Microsoft Cognitive Services and Google Cloud Machine Learning can help organizations what might be possible with their data. Having the opportunity to try the algorithms and services of different providers can allow businesses to find what works and allows for scaling before committing. When something is found that scales to requirements, the resources of these large providers are there to back up the scaling with compute capacity.

This was last updated in December 2017

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We at Kortical love the term AI as a Service, it hasn't caught on as much as we would like but hopefully with more articles like this it will! Thanks
The post is really brilliant and the information is very useful for beginners. I’ve read about IBM’s Watson in many articles but none of them gave me as satisfactory description as this did. Learning more with quality over quantity sounds fascinating.
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