The Doppler Quarterly Summer 2017 - Page 42

Both open source and proprietary artificial intelligence systems that support these types of predictions have been around for years. But in the past, the hardware and software costs of these systems have been prohibitive for most enterprises. Moreover, even if a company could afford it, they likely did not have the artificial intelligence talent required to design the prediction models, and manage the data science too. Enter cloud based artificial intelligence solutions from the big three public cloud providers AWS, Google and Microsoft. All are very different, but with some common advantages and limitations. The Advantages These systems are cheap to operate. You only pay a few dollars per hour, on average, to drive your very own AI application. Public clouds also provide cheap data storage. You can leverage true databases or storage systems for the data input into your AI-enabled applications. Finally, they all provide SDKs and APIs that allow you to embed AI functionality directly into your applications, while supporting most programming languages. The real value of AI technology is its use from within applications. For instance, the ability to determine in real time if a loan application is likely fraudulent, and to then provide a process to immediately deal with the issue, perhaps allowing an applicant to fix any errors and re-submit. These types of predictions are more focused on operations and transactions. The Disadvantages Artificial intelligence systems that reside on particular public clouds are fairly bound to those clouds. Therefore, if you use an artificial intelligence system on cloud A, then the data storage mechanism on cloud A will typically be natively supported. However, your enterprise database is not supported, unless you provide data integration between your on premises data storage system and those in the cloud. The key value for the cloud provider occurs when you take advantage of the native artificial intelligence system. It will then be in your best interest to take advantage of the native storage systems and native databases as well. Also, the applications live better on the cloud platform if they can frequently talk to the artificial intelligence models, which, in turn, often talk to the data. Get the hook? Of course, if you are already looking to move data, applications and other pro- cesses to the cloud, you’re fine. The artificial intelligence system can be accessed as a native cloud service. But if you’re working with hybrid- or multi- cloud based deployments, as most are, the separation of the data from the arti- ficial intelligence engine will be problematic, in terms of performance, cost and usability. Clearly, AI could be offered as a cloud provider’s loss leader, designed to attach more enterprises to that cloud. 40 | THE DOPPLER | SUMMER 2017