Building new NLP solutions with spaCy and Prodigy [EuroPython 2018 - Talk - 2018-07-26 - PyCharm [PyData]] [Edinburgh, UK] By Matthew Honnibal Commercial machine learning projects are currently like start-ups: many projects fail, but some are extremely successful, justifying the total investment. While some people will tell you to "embrace failure", I say failure sucks --- so what can we do to fight it? In this talk, I will discuss how to address some of the most likely causes of failure for new Natural Language Processing (NLP) projects. My main recommendation is to take an iterative approach: don't assume you know what your pipeline should look like, let alone your annotation schemes or model architectures. I will also discuss a few tips for figuring out what's likely to work, along with a few common mistakes. To keep the advice well-grounded, I will refer specifically to our open-source library spaCy, and our commercial annotation tool Prodigy. License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2018.europython.eu/en/speaker-release-agreement/