Today's digital world is an expanding frontier of emerging technologies. There are endless innovations, inspired by data, informed by data, enabled by data, and that create value from data. One thing we've seen more and more enterprises do to keep up with this digital revolution is the adoption of cloud services for a variety of IT functions, to an extent that modern approaches to building and running programs are often described as "cloud-native." According to Gartner, while only about 10 percent of enterprise-generated data is created and processed outside a traditional data center or cloud, this figure is expected to soar to 75 percent by 2025. The cloud alone simply isn't efficient enough to keep up with the volume and velocity of data that enterprises will be faced with as time goes on. So what is the missing piece to keeping up?

Model monitoring for predictive analytics at the edge begins with input, i.e. the data and how it is collected. I like to say, the Internet used to be a thing, but now, things are the Internet. In the Internet of Things, "things" are embedded with sensors, software, and other technologies for the purpose of connecting and exchanging data with other devices and systems. The data that is collected at the edge often needs to be processed in real-time in order to fuel predictive modeling or to reveal novel patterns in the data that may inspire questions we didn't think to ask about the things that we are monitoring. Some examples of edge applications are technologies like drones or self-driving cars, which operate autonomously through software controlled plans and onboard edge sensors, including GPS.