The value additive gains from enterprise use cases of cognitive computing and machine learning are as manifold as they are lucrative. Organizations can employ these technologies to optimize management of distributed retail or branch locations, supply relevant recommendations for tempting cross-selling and up-selling possibilities, and process workflows more effectively--and efficiently--at scale to boost customer satisfaction. What many are beginning to realize, however, is these gains are only manifested when firms can solve the core challenges that have been caveats for statistical Artificial Intelligence: model governance, explainability, and workflow analytics. The ModelOps movement either directly or indirectly addresses each of these three potential barriers to cognitive computing success. "As a vendor, if you haven't built this into your product natively, you're in trouble," Wilde reflected about ModelOps.

The ModelOps notion is so emblematic of AI because it gives credence to its full breadth (from machine learning to its knowledge base), which Gartner indicates involves rules, agents, knowledge graphs, and more. ModelOps is about more than simply operationalizing and governing AI models. Moreover, it involves doing so onsite while leveraging the advantages of the cloud and, when it comes to AI's machine learning prowess, with a range of approaches rooted in supervised, unsupervised, and even reinforcement learning. Implicit to these capabilities is the need to position machine learning models at the edge, supersede their traditional training data limitations (and methods), and imbibe everything from streaming to static data for a predictive exactness based on the most current data possible. Or, as SAS Chief Data Scientist Wayne Thompson put it, "Right now, most organizations are just checking the scores for the model and seeing if the model's scores have changed using an older offline model. What is state of the art is actually putting the model into the training environment, and deploy and train simultaneously and update the model's weights."

Everything Artificial Intelligence has ever been, hopes to be, or currently is to the enterprise has been encapsulated in a single emergent concept, a hybrid term, simultaneously detailing exactly where it is today, and just where it's headed in the coming year. The ModelOps notion is so emblematic of AI because it gives credence to its full breadth (from machine learning to its knowledge base), which Gartner indicates involves rules, agents, knowledge graphs, and more. ModelOps is about more than simply operationalizing and governing AI models. Moreover, it involves doing so onsite while leveraging the advantages of the cloud and, when it comes to AI's machine learning prowess, with a range of approaches rooted in supervised, unsupervised, and even reinforcement learning. Implicit to these capabilities is the need to position machine learning models at the edge, supersede their traditional training data limitations (and methods), and imbibe everything from streaming to static data for a predictive exactness based on the most current data possible.