As the applications for artificial intelligence (AI) and machine learning continue to grow, more and more businesses and enterprises are becoming interested in utilizing the power of AI and machine learning algorithms within their own operations. However, this isn’t always possible for a large number of businesses and organisations that may wish to do so.
Implementing and developing AI systems in-house is a long, intricate, and costly endeavor and it is rarely even considered by companies without the vast resources and finances required to do so. With that being said, service providers are now beginning to offer up artificial intelligence and machine learning as a service, much in the same way infrastructure and software have been available for some time now.
In this article, we’ll be taking a look at what artificial intelligence-as-a-service is and how it provides AI and machine learning resources to businesses and organisations for significantly less than would be possible when developing an in-house AI system.
Off The Shelf AI?
So how does off the shelf AI work? Essentially, third party providers are now beginning to offer AI and machine learning algorithms that cater towards a customer’s specific needs, as opposed to those same customers needing to spend a vast amount of time and money in developing and building their own.
Those with the technical know-how in developing AI and machine learning systems are currently fairly few and far between and companies with the time and resources to start to invest in such systems, while maybe willing, still need to find an individual or business with the expertise to help them do so.
In order to overcome this issue, companies such as Amazon, Google, Microsoft and IBM have begun to offer up AI services in the cloud to enable everyone to utilize and take advantage of artificial intelligence and machine learning. Several different variants of both AI and machine learning tools are available from these and other providers for a wide range of different applications.
As was the case with both infrastructure and software, Artificial intelligence and machine learning-as-a-service will continue to develop new tools and services that provide for its customers on either a subscription or pay as you go basis.
What’s On Offer?
There are three main categories of artificial Intelligence-as-a-service providers are now offering through their cloud services. These are best defined as AI tools which include data preparation, libraries, and frameworks; AI services which include cognitive computing and conversational AI; and AI infrastructure that includes AI data and compute service functions.
The AI tools on offer from service providers are generally particularly focused on data and compute elements and are increasingly being used as tools for scientists and developers and it has been estimated that their continued use will also drive the consumption of containers, storage, databases, and virtual machines.
These tools will also help to reduce the complexity of training machine learning models by taking advantage of scale-out infrastructure in the architecture and use this to create a multi-tenant development environment. These tools will also help to improve the quality of the data being used to train these machine learning models.
Using AI-as-a-service, service providers are able to offer up APIs and AI and machine learning services to customers that can be used without consumers needing to spend the time in training their own custom machine learning models. Custom cognitive computing is another way in which service providers are helping customers utilize AI through APIs by allowing them to use their own custom data sets to train cognitive services.
This removes much of the complexity in choosing the right machine learning algorithm and data set for a consumer’s bespoke requirement as well as removing the need for them to build their own custom machine learning models.
The Future of of AI?
As mentioned throughout this article, there are various benefits to utilizing artificial intelligence-as-a-service, including the fact that a significantly higher percentage of businesses and organisations now have access to these technologies. While AI and machine learning are still fairly young in their evolution, it seems reasonable to expect that, for most companies, the majority of enterprises will choose to engage with these technologies in this or similar manner.
As we begin to see AI systems mature, however, it become increasingly likely that the cost of their development will inevitably become cheaper and less complex, allowing a higher number of businesses to begin to experiment with building their own. This in turn could cause the development of even more AI and machine learning applications and this continue to drive further development.
While AI-as-a-service is an extremely useful model for enabling smaller, less resourced companies and enterprises to engage with cutting edge technologies like artificial intelligence, it seems unlikely to become anything more than a transitional technology until such time in that AI and machine learning systems are both easier to acquire and simpler to configure.
