One the most important deployed applications of machine learning at this point would be web search, so papers relating to that (PageRank, etc) would probably score highly.
I’d expect some papers in spam filtering (which was pretty important / interesting as a machine learning topic at the time) to maybe meet the threshold.
Lastly for now, I think Kevin Murphy’s excellent 2012 machine learning textbook: “Machine Learning: A Probabilistic Perspective” has a ton of sidebars and sections for applied machine learning systems, and would probably worth going through.
One the most important deployed applications of machine learning at this point would be web search, so papers relating to that (PageRank, etc) would probably score highly.
I’d expect some papers in spam filtering (which was pretty important / interesting as a machine learning topic at the time) to maybe meet the threshold.
TD-Gammon would probably qualify in the world of RL https://en.wikipedia.org/wiki/TD-Gammon
DistBelief just barely predates that, and since it’s basically directly in the lineage to modern deep learning, I think might qualify https://en.wikipedia.org/wiki/TensorFlow#DistBelief
Leo Gao’s 2010′s summary post has some citations that directly qualify https://bmk.sh/2019/12/31/The-Decade-of-Deep-Learning/
Lastly for now, I think Kevin Murphy’s excellent 2012 machine learning textbook: “Machine Learning: A Probabilistic Perspective” has a ton of sidebars and sections for applied machine learning systems, and would probably worth going through.
Edit to add: this source might also be useful https://mlstory.org/