2021 Trends in AI and ML: The ModelOps Movement

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."