In recent years, tech giants such as Amazon, Google, Microsoft and IBM (along with a slew of startups) have all begun to offer what’s known as Artificial Intelligence as a service (AIaaS). These services, in a nutshell, make a wide range of AI algorithms available to the public. Examples for this are algorithms for classification, regression, and Deep Learning – a modern learning algorithm that relies on Artificial Deep Neural Nets. As more and more companies begin to make use of AlaaS, a better understanding of how it can be best integrated into your own business is the difference between having a massive cost-saver and a massive headache.
Involving AI
Companies once had to spend a lot of time producing their own AI applications, and did so at great expenditure. Because setting up an AI infrastructure as well as to develop AI algorithms on one’s own is difficult, AIaaS delivers a working solution effortlessly – saving both time and money. This ‘AI off the Shelf,’ as it’s sometimes called, is able to do this by providing already-set up infrastructure and pre-trained algorithms, which reduce development time, as well as the amount of required resources across the board to complete complex tasks.
AIaaS is also built upon previously existing services. Cloud hosters have been offering IaaS (Infrastructure as a Service) and SaaS (Software as a Service) for a while now. This concept is now applied to AI. In addition to reduced development time and costs, it also reduces investment risk and increases strategic flexibility. But companies also need to consider AlaaS’s disadvantages. These include dependency on a service provider and a working data connection capable of some considerable speed. There is also the risk of reduced data security and a degree of standardization that puts constraints on innovation.
Levels of AI
On top of advantages and disadvantages, it’s important to distinguish two levels of AI: high and low-level AI. High level AI solves complex, but ultimately standardized problems. An example of high level AI is face recognition software. Because the user interface is simple – put image in, receive yes/no for an answer – non-AI experts are capable of using high level AI with little difficulty.
Low level AI, in contrast, is set up to solve different tasks with differing requirements. Examples of this include logistic regression which can be used for churn prediction or detecting fraud. Properly wielding low level AI requires expert knowledge in areas such as data pre-processing, model training, parameter optimization and evaluation. The long processing pipeline means a higher likelihood of making a mistake at the different stages of problem solving, and therefore it’s often impossible to put low level AI to use without AI experts at the ready.
Now that falling costs and increasing capabilities in AI are letting a more diverse collection of companies (many of which are not necessarily technology-focused) employ these two forms of AIaaS, knowing what’s needed to keep them up and running is critical.
Choosing the Right AI
In a first step, it’s important to choose the right service for your business. This can be difficult, since AlaaS providers don’t disclose their implementations of algorithms. Normally, all that is documented is the API of a given algorithm. An uninformed purchase is also hardly inevitability when it comes to AI. Like any software, a business is better off testing the service systematically before they commit to buying it.
In low level AI, many clients get stuck in setting up a fitting processing pipeline. There are many intricate steps to this process, that are implemented differently by providers. Therefore, it is highly recommended that a company compares the services to a self-coded implementation before signing off on anything. Test. Compare. Repeat.
This is crucial, because AI algorithms are in the end just software that can be buggy. One way to get around this is by inserting your own code which is allowed by some of the service providers. This can be helpful, but only if a company has competent teams that know what they would like to get out of making specific changes.
Conclusion
AlaaS, when used properly, is a remarkable tool that enables nearly all companies to greatly expand their capabilities by putting AI to good use at a minimal expenditure of time, hardware and staff. The diversity of its offerings is as much an asset as it is an obstacle. Proper research is critical to purchasing the right AI service and employing it for the right purposes. Testing products and consulting with different service providers is a must – especially for new technologies such as these where a few bugs are still something to be prepared for. Do that, and a whole new world of possibilities opens up.
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