Stu is a technologist and entrepreneur who has been innovating and commercializing large scale model intensive production systems for over two decades.
I've been having a lot of meetings over the last several months with senior executives at large enterprises who are trying to rationalize their organizations' investments in AI. Many of them have approved hefty budgets to drive their company's AI journey, and are being pressed to keep upping the ante. Most share a general sense that their AI programs are adding value-driving top line revenue, automating manual processes and providing a business edge. But few are able to answer some pretty basic questions like:
-What's the ROI for our AI investments?
-Which programs are meeting expectations, and which are not?
-Which programs should be prioritized for additional investment?
-What's the extent of our exposure to AI compliance risks?
The challenge is becoming more acute as AI enters the arms-race phase, with business units clamoring for 7-figure (and higher) increases in AI budgets to keep up with competitors. Data science teams are using an expanding variety of tools to develop models faster and push them into production, often without visibility or oversight at the enterprise level. Against this backdrop, many execs feel they need a better handle on both the benefits and the risks of their AI programs before they pump in yet more money. But for all of the investment in AI, this critical need has yet to be addressed in most companies, leaving execs in the dark and struggling to make the best decisions.
Over the last few years, hyperscale vendors and makers of data science and machine learning (DS/ML) platforms have been adding so-called "MLOps" capabilities to their products that help data scientists during model development and handle a few aspects of deploying models into production. But what these products - and execs - still lack is a tool that sits across the enterprise and provides a complete, real-time, evergreen "source of truth" covering all aspects of all models in production, regardless of their type, where they were developed (or purchased or rented) or where they're deployed, and that shows, for every model, it's business contribution and risk posture. What they lack, in a word, is ModelOps.
Our ModelOp Center platform fully and uniquely addresses this need. Our Executive Visibility (EV) dashboards integrate with all DS/ML platforms and deployment environments - as well as enterprise IT and business systems - and provides a comprehensive, real-time and historical overview of the operational, business and compliance status of all production models, updated continuously and automatically. At a glance senior management can see the ROI for AI investments and spot models at risk of non-compliance, before they become serious liabilities. As a result, they can make much better decisions about where to place their investments.
Of particular note, deploying ModelOp Center EV isn't a heavy lift. Most organizations are up and running in just a few weeks. So organizations can keep moving fast, but do so with much greater insight and confidence - instead of flying blind.
