Stay informed about AI Governance, Enterprise AI, Responsible AI, and ModelOp Center
It is important to know the difference between MLOps and ModelOps because neither is a substitute for the other.
The possibilities for AI use grow almost daily, so it’s important not to limit innovation. Unfortunately, many organizations do just that by tethering themselves to proprietary tools and solutions.
Recently I had the pleasure of opening a panel discussion on “Governance and Risk Management for AI and ML Models.” The discussion centered on the challenges wrought by the rapidly growing use of artificial intelligence (AI) across their enterprises.
Modern organized enterprises recognize that the adoption of a data-driven strategy is crucial to compete in an increasingly digitalized market.
Even those companies that have mature governance practices in place are facing new challenges wrought by the rapid and broad adoption of AI. Further, large companies in less-regulated industries may be especially vulnerable to new risks that stem from wide use of AI technology.
