This document discusses machine learning systems and software engineering practices. It covers topics like version control with Git, automated feature management, input schemas, glue code versus pipelines, and using assertions. References are provided for papers on machine learning technical debt and practices for handling click prediction models at large internet companies.

1. Wush Wu

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3. Ward Cunningham (1992)·
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5. API
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7. (Sculley et al. 2014)
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A B ==>
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10. A B C D ...
A B C D ...
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13. public/protected/private
IDE
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19. Deep Insight
http://research.google.com/pubs/pub41159.html
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22. Automated Feature
Management
Ad Click Prediction: a View from the Trenches·
Chap 8-
Signals consumed by new and active models
are vetted by an automatic system of alerts. Different learn-
ing platforms share a common interface for reporting signal
consumption to a central index. When a signal is depre-
cated (such as when a newer version is made available), we
can quickly identify all consumers of the signal and track
replacement eforts.
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23. Input Schema
.proto
.proto
jenkins task
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27. Glue
Code
D. Sculley 2014: 5% 95%·
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28. LiblineaR,
libffm, ...
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29. Glue Code
vs
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30. Pipeline
Jungle
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Knight Capital's
45 4.6
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33. Glue Code Pipeline Jungle·
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34. Version Control Git
API
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Git-
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D. Sculley: Google-
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36. Assertion·
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Git-
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37. Reference
D. Sculley, et al. (2014) Machine Learning: The High
Interest Credit Card of Technical Debt.
H. Brendan McMahan et al. (2013) Ad Click Prediction: a
View from the Trenches.
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38. Q&A
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