How should IT leaders and professionals go about selecting and delivering the technology required to deliver the storied marvels of artificial intelligence and machine learning? AI and ML require having many moving parts in their right places, moving in the right direction, to deliver on the promise these technologies bring -- ecosystems, data, platforms, and last, but not least, people.
Is there a way for IT leaders to be proactive about AI and ML without ruffling and rattling an organization of people who want the miracles of AI and ML delivered tomorrow morning? The answer is yes.
The authors of a recent report[1] from MIT Sloan Management Review and SAS advocates a relatively new methodology to successfully accomplish the delivery AI and ML to enterprises called "ModelOps[2]." While there a lot of "xOps" now entering our lexicon, such as MLOps[3] or AIOps[4], ModelOps is more "mindset than a specific set of tools or processes, focusing on effective operationalization of all types of AI and decision models."
That's because in AI and ML, models are the heart of the matter, the mechanisms that dictate the assembly of the algorithms, and assure continued business value. ModelOps, which is short for :model operationalization, "focuses on model life cycle and governance; intended to expedite the journey from development to deployment -- in this case, moving AI models from the data science lab to the IT organization as quickly and effectively as possible."
In terms of operationalizing AI and ML, "a lot falls back on IT," according to Iain Brown, head of data science for SAS, U.K. and Ireland, who is quoted in the report. "You have data scientists who are building great innovative things. But unless