The state of NLP in production 🥽

NLP in production vs real life

Transcript

1. The state of NLP in production
   

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3. Python Mauritius Usergroup
   site
   fb
   linkedin
   mailing list
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4. url
   pymug.com site
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5. About me
   compileralchemy.com
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6. slides
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7. The state of NLP in production
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8. Hardest part of a real-world project
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9. ?
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10. Is it cooking up an awesome model?
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11. No, the world is more complex than this
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12. Elements of an NLP project
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13. NLP project
    gather data
    clean
    store
    train
    use model
    retrain model
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14. gather data
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15. Toy project
    use curated data set
    quick extraction
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16. Real project
    a lot of data needed
    data corresponds to business case. data probably does
    not exist
    speed of data gathering
    find ingenious / better ways of getting data
    automate collection
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17. clean/preprocess data
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18. Toy project
    use an existing parser / curator e.g. NLTK existing options
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19. Real project
    use a parser intended for it, several custom steps
    parallel processing of data
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20. store data
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21. Toy project
    laptop
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22. Real project
    cloud database
    hot / cold data
    TTL
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23. training
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24. Toy project
    use laptop / external GPU
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25. Real project
    on cloud training
    on cloud knowledge
    cross-cloud skills
    fault tolerance
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26. use model
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27. Toy project
    local website / code
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28. Real project
    continuation of pipeline
    web service architecture
    devops / deploy
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29. retraining
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30. Toy project
    euhh this even exists????
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31. Real project
    learn cloud offerings for continuous learning
    ways to retrain / fine tune
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32. It's more than serving a model
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33. Operation model
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34. [ pipeline ]
    data collection --- process --- train -<-
    | |
    --------------------------- model ^
    | | |
    | --->---
    V
    web service [pod] [pod] --- happy user
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    -> users service [pod] [pod]
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    -> db service [pod]
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35. skills chart
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36. skills
    --------------- ---------------
    | | | |
    | backend | | devops |
    | | | |
    --------------- ---------------
    --------------- ---------------
    | | | |
    | backend | | data eng |
    | | | |
    --------------- ---------------
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37. skills
    --------------- ---------------
    | | | |
    | backend | | devops |
    | | | |
    --------------- ---------------
    web service deploy
    --------------- ---------------
    | | | |
    | ml | | data eng |
    | | | |
    --------------- ---------------
    models pipelining
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38. code blueprint
    [ architecture repos ]
    [ pipeline repos ]
    [ ml repos ]
    [ backend repos ]
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39. Tools
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40. Pandas
    Good queries
    Much resources
    Read SQL
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41. Dask
    Good for it's purpose: Parallelize tasks
    Poor docs
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42. Polars
    Awesome parallelizations
    Great docs
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43. NLTK
    use spacy if possible
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44. Notebooks
    great for cloud
    used in production on the cloud
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45. Advice to research / scientists folks
    keep everything clean
    people will come after you
    always in hurry / messy / i'll clean it later mood
    good practices? is this phrase in the korean dictionary?
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46. General advices
    have great docs
    good onboarding
    have great standards
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47. Keep learning!
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