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Best Practices for Using Machine Learning in Bu...
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szilard
November 04, 2018
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Best Practices for Using Machine Learning in Businesses in 2018 - Keynote at Budapest BI Forum Conference - Budapest, November 2018
szilard
November 04, 2018
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Transcript
Best Practices for Using Machine Learning in Businesses in 2018
Szilárd Pafka, PhD Chief Scientist, Epoch (USA) Budapest BI Forum Conference November 2018
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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
https://twitter.com/baroquepasa/
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y = f (x1, x2, ... , xn) Source: Hastie
etal, ESL 2ed
y = f (x1, x2, ... , xn)
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Source: Yann LeCun
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2018?
2018?
#1 Use the Right Algo
Source: Andrew Ng
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#2 Use Open Source
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in 2006 - cost was not a factor! - data.frame
- [800] packages
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#3 Simple > Complex
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10x
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#4 Incorporate Domain Knowledge Do Feature Engineering (Still) Explore Your
Data Clean Your Data
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#5 Do Proper Validation Avoid: Overfitting, Data Leakage
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#6 Batch or Real-Time Scoring?
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https://medium.com/@HarlanH/patterns-for-connecting-predictive-models-to-software-products-f9b6e923f02d
https://medium.com/@dvelsner/deploying-a-simple-machine-learning-model-in-a-modern-web-application-flask-angular-docker-a657db075280 your app
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R/Python: - Slow(er) - Encoding of categ. variables
#7 Do Online Validation as Well
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https://www.oreilly.com/ideas/evaluating-machine-learning-models/page/2/orientation
https://www.oreilly.com/ideas/evaluating-machine-learning-models/page/2/orientation
https://www.oreilly.com/ideas/evaluating-machine-learning-models/page/2/orientation https://www.slideshare.net/FaisalZakariaSiddiqi/netflix-recommendations-feature-engineering-with-time-travel
#8 Monitor Your Models
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https://www.retentionscience.com/blog/automating-machine-learning-monitoring-rs-labs/
https://www.retentionscience.com/blog/automating-machine-learning-monitoring-rs-labs/
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20% 80% (my guess)
20% 80% (my guess)
#9 Business Value Seek / Measure / Sell
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#10 Make it Reproducible
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Cloud (servers)
ML training: lots of CPU cores lots of RAM limited
time
ML training: lots of CPU cores lots of RAM limited
time ML scoring: separated servers
ML (cloud) services (MLaaS)
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“people that know what they’re doing just use open source
[...] the same open source tools that the MLaaS services offer” - Bradford Cross
Kaggle
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already pre-processed data less domain knowledge (or deliberately hidden) AUC
0.0001 increases "relevant" no business metric no actual deployment models too complex no online evaluation no monitoring data leakage
Tuning and Auto ML
Ben Recht, Kevin Jamieson: http://www.argmin.net/2016/06/20/hypertuning/
GPUs
Aggregation 100M rows 1M groups Join 100M rows x 1M
rows time [s] time [s]
Aggregation 100M rows 1M groups Join 100M rows x 1M
rows time [s] time [s] “Motherfucka!”
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API and GUIs
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AI?
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How to Start?
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