There is a seemingly myriad of terms to describe people who interact with models

There is a seemingly myriad of terms to describe people who interact with models. Just a few terms that are currently in usage include researchers, data scientists, machine learning researchers, machine learning engineers, data engineers, infrastructure engineers, DataOps, DevOps, etc. Both Miner and Presser commented upon and agreed that before any assignment of any term, the work itself existed previously. Presser defines data engineering as embodying the skills to obtain data, build data stores, manage data flows including ETL, and provide the data to data scientists for analysis. Presser also indicated that data engineers at large enterprise organizations also have to be well versed in “cajoling” data from departments that may not, at first glance, provide it. Miner agreed and indicated that there is more thought leadership around the definition of data science versus data engineering which contributes to the ambiguity within the market. — https://blog.dominodatalab.com/collaboration-data-science-data-engineering-true-false/
Up Next Next → I’ve never heard of anybody having a data engineering undergrad class, but you’re starting to hear data science classes pop up https://blog.dominodatalab.com/collaboration-data-science-data-engineering-true-false/ ← Previous Over the past five years, we have heard many stories from data science teams about their successes and challenges when building, deploying, and monitoring models https://blog.dominodatalab.com/collaboration-data-science-data-engineering-true-false/
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