The Doppler Quarterly Summer 2017 - Page 40

Their offers often pair the ability to efficiently leverage artificial intelligence services with big data management systems that provide the source of the data, and thus the source of the patterns. It’s important to consider all aspects of your requirements, and how the public cloud provider can best meet them. This goes beyond artificial intelligence, to the way in which data, middleware and analytical services work together to solve real business problems. Artificial intelligence systems offered by public cloud providers include SDKs (software developer kits) and APIs that allow developers to embed AI within their applications. This bridges the gap between the capabilities of artificial intelli- gence, and the actual real world use of this technology. An example would be the ability to determine if a loan application is fraudulent, based upon past and cur- rent patterns, as applied to the data that’s within the loan application. There are downsides to AI on a public cloud. First, you must leverage some- thing that’s basically native to the public cloud provider, which means you have to port the data to other clouds, or bring it back on premises, which could be problematic. Second, many enterprises have a tendency to overuse AI, lever- aging it for applications that don’t actually need its capabilities. For instance, AI is overkill for simple business processes that are more procedural in nature. Artificial Intelligence Usage Patterns All artificial intelligence models are not the same. They are all defined to learn, but provide different solution patterns. Most cloud providers, including AWS, Google and Microsoft, provide support for three types of predictions. They have different names, but they boil down to three types: binary prediction, cat- egory prediction and value prediction (Figure 2). Let’s explore the potential use cases of each. Figure 2: AI Prediction Types 38 | THE DOPPLER | SUMMER 2017 Value Prediction Category Prediction Binary Prediction