Strategies & Outcomes

AI driven collaboration of multiple data science models for greater good

In a typical data science ecosystem, models evolve, phase out, induct newer ones, and so, businesses employ mechanisms to combine multiple prescriptions. In many cases the prescriptions are combined manually at operations or through custom applications that are somewhat harder to evolve, when data science models upgrade their prescription interfaces. Therefore it is essential that we devise mechanisms to bring not only consistency of the inputs, but also consistency of the prescriptions from the models themselves.

Don't let your enterprise cloud adoption pause or slow down your data science projects

Data science models play a key role in any Artificial Intelligence ecosystem. Their prescriptions are vital part of reasoning engines. Negative impacts to data science efforts translate to enterprise AI rollout

Now you have a bunch of data science models. Are their prescriptions consistent with one another?

What happens when more and more data science models make isolated decisions, sometimes at rapid rates, with
minimal or no explanations? Can multiple data science model responses be made to be consistent, collaborative,
transparent and yet be responsive?