  Data-centric AI is a new and exciting research topic in the AI community, but
many organizations already build and maintain various "data-centric"
applications whose goal is to produce high quality data. These range from
traditional business data processing applications (e.g., "how much should we
charge each of our customers this month?") to production ML systems such as
recommendation engines. The fields of data and ML engineering have arisen in
recent years to manage these applications, and both include many interesting
novel tools and processes. In this paper, we discuss several lessons from data
and ML engineering that could be interesting to apply in data-centric AI, based
on our experience building data and ML platforms that serve thousands of
applications at a range of organizations.
