Stay informed about AI Governance, Enterprise AI, Responsible AI, and ModelOp Center
Welcome to the next post in the series about living life off the grid in the mountains of Colorado while working as the CTO of ModelOp.
Through ModelOps, teams can be positioned to fully unleash the value of their AI investments, while addressing requirements to boost trust and minimize risk.
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.
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