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I think ML as a field just needs to really mature. A lot of the work feels super hacky, sort of a mix between research, demos, and production.
by aliabd 6y ago
I think ML as a field just needs to really mature. A lot of the work feels super hacky, sort of a mix between research, demos, and production.
- Guest42 6y agoRight. I think it ignores the importance of the data when building a model. Even great data can lead to difficult modeling scenarios. The premise that a “solution” can guarantee (or even partially guarantee) to be useful is misleading.
- fuzzybear3965 6y agoI don't think the author would disagree with you. In fact, I think this article was highlighting one specific area in which the field could improve.
- mlthoughts2018 6y agoMore specifically, at least in industry, we need SRE / ops support to mature. Taking a team of people who are highly specialized at the research layer of a statistical computing problem, then treating them like they are immature when they get massively overloaded also solving credential management, Kubernetes config, web service hardening, efficient data pipelining, etc. etc. is just such a whiny and immature thing to see come out of infra / ops team leaders, that leads to burning out ML engineers, and wasting a lot of money failing to extract value from their comparative advantage for the business just because infra / ops leaders can’t get it together and solve ML coordination problems.