Machine Learning as a Service (MLaaS)


Machine learning as a service (MLaaS) is an array of services that provide machine learning tools as part of cloud computing services. 



  • Advanced infrastructure: AI applications, particularly machine learning and deep learning applications, perform best on servers with multiple, very fast graphics processing units (GPUs) that run workloads in parallel. However, those systems are very expensive, putting them out of reach for many organizations and use cases. AI as a service gives organizations access to those superfast computers at a price they can afford.

  • Low costs: Not only does "AI as a service" eliminate the need to pay for expensive hardware upfront, it also allows organizations to pay only for the time that they need that hardware. In cloud computing jargon, most AI workloads are said to be "bursty," that is, they require a whole lot of computing power for a short period of time. AI as a service charges organizations only for what they use, lowering their costs significantly.

  • Scalability: Like other types of cloud services, AI as a service makes it very easy to scale. Often organizations start with a pilot project that allows them to see how AI could be useful. With AI as a service, they can quickly move that pilot project into full production and scale up as demand grows.

  • Usability: Some of the best artificial
    intelligence tools are available with open source licenses, but while they are inexpensive, these open source AI tools aren't always very easy to use. The cloud AI services generally make it easier for developers to access artificial intelligence capabilities without requiring them to be experts in the technology.


AIaaS Limitations


The two biggest drawbacks of AI as a service are two issues that are common to all cloud computing services: security and compliance.

Many AI applications — especially applications that incorporate machine learning capabilities — rely on vast quantities of data. If that data is going to reside in the cloud or be transferred to the cloud, organizations need to make sure that they have in place adequate security measures, including encryption both at rest and in transit.

In some situations, regulations may prevent some types of sensitive data from certain industries from being stored in the cloud. Other laws require that some data remains within the borders of the country where it was originated. In these cases, it may not be possible to use an AI as a service offerings for those specific use cases.

Another potential drawback is that AI as a service can be very complex. Organizations will have to invest time and effort in training and/or hiring staff with artificial intelligence and cloud computing skills. However, many organizations believe that this hurdle can be easily overcome and that AI as a service will pay off in the long run.

Credit: Data Scientist, Riminder Intelligence

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