The key to creating value with data science is to always envision how a project or product will be implemented and utilize a smooth system of fast deployment.
When developing data science functions, data leaders from the onset should enable career pathways for their data talent.
Model governance leaders need to not only understand all of the algorithms and data, but they need to understand regulation and legislation for both the present and the future.
Data Scientists have to constantly talk with stakeholders, consistently helping them understand the value of data science as it relates to their business problems, and data scientists need to understand those business problems and everyday concerns. Great stakeholder relationships can really only happen when both parties understand where they are each coming from.
We want to build great models that we can trust and that are diligent and sound, but if they aren’t deployable, then they are pointless. As soon as the first model reaches a stage where you consider how it will be deployed, the ML ops function can help data scientists build models that are ready for production throughout the process, both from a code perspective and from a data perspective.