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.
See the blog entry for details:
What to expect
Technology platform engineers, Self-service champions from data science teams, Enterprise architects, Cloud partners
Impact analysis on each data science models prescription, large scale business processes collaboration and automation, system-wide optimization, data and process driven culture
Conflict resolution strategies alignment with business priorities, AI technologies learning curve