Is "the sexiest job of 21st century", the Data Scientist, about to be automated? How & when can AutoML tools help on a typical machine learning lifecycle? What AutoML challenges are still open & what ML work will remain in the foreseeable future? Most importantly… will robots get all the fun & sex appeal? :) Some questions we'll try to tackle on this session. *-Robots are not allowed in this session

1. Data Research @ DevScope
Dewan Fayzur, Data Scientist @ DevScope
DSPT #27, 20180417

2. Data R&D @ DevScope
#PowerBI #SQLServer #Web
#Analytics #Azure #Microsoft
#MachineLearning #AutoML
#R #Linux #Dataiku #Docker
#Python #Coaching #Learning
also very often a #DataSkeptic 
twitter.com/rquintino
rquintino.wordpress.com
rquintino@gmail.com
“jack of all trades (and master of none)“
1. a person who can do many different types
of work but who is not (necessarily…) very
competent at any of them…

3. No templates used

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6. Tiny AutoML Research Tip…

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10. A few shades of AutoML…

11. Machine Learning
GUIs - Productivity

21. Model/Pipeline Selection,
Hyper-Parameter Tuning

22. H2O AutoML

23. H2O AutoML

24. • H2O AutoML
H2O Flow UI

25. •
Tpot

26. •
Tpot

31. Commercial AutoML
Kaggle “Grandmasters”

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H2O Driverless AI

36. DataRobot

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49. • https://automl.info/
• http://www.ml4aad.org/
• https://competitions.codalab.org/competitions/17767
• https://sites.google.com/site/automl2017icml/
• www.openml.org

51. rui.quintino@devscope.net

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