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Don't forget data cleaning. A huge issue I've seen is just getting sufficient data of a high enough quality. Also, (for supervised classification problems) la
by jnwatson 6y ago
Don't forget data cleaning. A huge issue I've seen is just getting sufficient data of a high enough quality.
Also, (for supervised classification problems) labelling is a big problem.
It is almost as if we need a "data janitor" title.
- ajb 6y agoPhht you don't want to call it data janitor; no-one good will want that title. At least call it Data Integrity Engineer or something reasonably high-status.
- WrtCdEvrydy 6y agoMachine Learning Data Integrity Engineer
- anthuswilliams 6y agoAt my company we have just created a position called Data Steward.
- ska 6y agoData Sanitation Engineer :)
- google234123 6y agoData cleaning always sounded suspicious to me.
- jboggan 6y agoAs a data engineer I've often billed myself as the Charlie Kelly of data
- Breza 6y agoThis is a hugely important point. Having the right data is most of the battle in the typical project. I'm fortunate to run the data science department at a company with an amazing and supportive team of devs. We work together to make sure that all data is recorded in the most useful way and we talk regularly. Without their support, my models wouldn't be very useful.