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Machine Learning in Production with R or Python...

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June 08, 2017
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Machine Learning in Production with R or Python - Budapest Data Forum - June 2017

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June 08, 2017
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Transcript

  1. Machine Learning in Production Szilárd Pafka, PhD Chief Scientist, Epoch

    Budapest Data Forum June 2017

  2. Machine Learning in Production with R Szilárd Pafka, PhD Chief

    Scientist, Epoch Budapest Data Forum June 2017

  3. Machine Learning in Production with R or Python Szilárd Pafka,

    PhD Chief Scientist, Epoch Budapest Data Forum June 2017

  4. Machine Learning in Production with R or maybe Python Szilárd

    Pafka, PhD Chief Scientist, Epoch Budapest Data Forum June 2017
  5. None

  6. Disclaimer: I am not representing my employer (Epoch) in this

    talk I cannot confirm nor deny if Epoch is using any of the methods, tools, results etc. mentioned in this talk
  7. None

  8. http://datascience.la/meetup-machine-learning-in-production-with-szilard-pafka/

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  22. Aggregation 100M rows 1M groups Join 100M rows x 1M

    rows time [s] time [s]

  23. Aggregation 100M rows 1M groups Join 100M rows x 1M

    rows time [s] time [s]
  24. None
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  28. binary classification, 10M records numeric & categorical features, non-sparse

  29. http://www.cs.cornell.edu/~alexn/papers/empirical.icml06.pdf http://lowrank.net/nikos/pubs/empirical.pdf

  30. http://www.cs.cornell.edu/~alexn/papers/empirical.icml06.pdf http://lowrank.net/nikos/pubs/empirical.pdf

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  36. EC2

  37. n = 10K, 100K, 1M, 10M, 100M Training time RAM

    usage AUC CPU % by core read data, pre-process, score test data

  38. n = 10K, 100K, 1M, 10M, 100M Training time RAM

    usage AUC CPU % by core read data, pre-process, score test data
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  46. 10x

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  52. http://datascience.la/benchmarking-random-forest-implementations/#comment-53599

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  60. Best linear: 71.1

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  63. learn_rate = 0.1, max_depth = 6, n_trees = 300 learn_rate

    = 0.01, max_depth = 16, n_trees = 1000
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