Recently, I had the pleasure of attending the Gartner Data and Analytics conference. The sessions featured representatives from McDonald's, Bank of America, Samsung, Pepsi, and many others, who shared their methods for using AI/ML in the enterprise, some of their pain points, and their ongoing journeys to become AI-first companies.
Two stats stood out above the rest of the show. First, Gartner is tracking more than 300 MLOps companies. Next, in a recent study, Gartner found that only 54 percent of models make it into production, practically unchanged from a similar study they did in 2020.
Yet, the company representatives I spoke to would regularly cite even lower numbers. Some put the figure of models that make it from experiment to production in the single digits. MLOps platforms are, ostensibly, designed to raise this statistic.
I sense a paradox. While there's obviously a problem within enterprises to bring models into production, few of the platforms today appear to solve that problem.
We've found this is for a few reasons:
Here at Vianai, we benefit from senior leaders who understand how fragmented and complex an enterprise's IT landscape can become. This knowledge and further research went directly into the design of our applications and our platform.
We have sought to solve these problems in many ways. One is by showing models in production, with functionality that non-technical folks can use. Another is through interoperability built into our platform the start - we are completely modular and operate in any cloud provider. Finally, we have developed proprietary techniques for understanding risk in a model and in the underlying data.