Data Science Hype vs Reality: "Big Data" vs Single Machine Tools - Predictive Analytics World Conference - May 2017, San Francisco

Transcript

1. Data Science Hype vs Reality: "Big Data" vs Single Machine Tools Szilárd Pafka, PhD Chief Scientist, Epoch Predictive Analytics World Conference May 2017, San Francisco
3. 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
8. Example #1
28. https://deads.gitbooks.io/paratext-bench/content/teaser.html June 2016
29. Aggregation 100M rows 1M groups Join 100M rows x 1M rows time [s] time [s]
30. (largest data analyzed)
31. (largest data analyzed)
32. (largest data analyzed)
34. data size [M] training time [s] 10x Gradient Boosting Machines
37. linear tops off (data size) (accuracy)
38. linear tops off more data & better algo (data size) (accuracy)
39. linear tops off more data & better algo random forest on 1% of data beats linear on all data (data size) (accuracy)
40. linear tops off more data & better algo random forest on 1% of data beats linear on all data (data size) (accuracy)
50. Summary / Tips for analyzing “big” data: - Get lots of RAM (physical/ cloud) - Use R/Python and high performance packages (e.g. data.table, xgboost) - Do data reduction in database (analytical db/ big data system) - (Only) distribute embarrassingly parallel tasks (e.g. hyperparameter search for machine learning) - Let engineers (store and) ETL the data (“scalable”) - Use statistics/ domain knowledge/ thinking - Use “big data tools” only if the above tips not enough
51. Example #2
53. I usually use other people’s code [...] I can find open source code for what I want to do, and my time is much better spent doing research and feature engineering -- Owen Zhang
54. binary classification, 10M records numeric & categorical features, non-sparse
55. http://www.cs.cornell.edu/~alexn/papers/empirical.icml06.pdf http://lowrank.net/nikos/pubs/empirical.pdf
56. http://www.cs.cornell.edu/~alexn/papers/empirical.icml06.pdf http://lowrank.net/nikos/pubs/empirical.pdf
63. EC2
64. n = 10K, 100K, 1M, 10M, 100M Training time RAM usage AUC CPU % by core read data, pre-process, score test data
65. n = 10K, 100K, 1M, 10M, 100M Training time RAM usage AUC CPU % by core read data, pre-process, score test data
73. 10x
77. http://datascience.la/benchmarking-random-forest-implementations/#comment-53599
84. Best linear: 71.1
87. learn_rate = 0.1, max_depth = 6, n_trees = 300 learn_rate = 0.01, max_depth = 16, n_trees = 1000
90. ...
97. Summary