1. Python at Yhat
Dev StackUp
August 2013

2. Agenda
About Yhat
How we use Python
Questions

3. We need to reduce
churn. Okay. I'll look into it.
Lots of conversations like this

4. I figured out that....some
complex stuff about
vector space that'll
improve...
....and that's how we'll
reduce churn.
Sounds good. Let's
do that...
The "a ha" moment isn't the end.

5. Now what?
Any of you know
what Gradient
Boosting is?
So when can we go
live with the new
model?

7. What goes on in the Kludge?
Rewriting Code
Batch Jobs
PMML

8. How can we...
- eliminate implementation time

9. How can we...
- eliminate implementation time
- let data scientists use their favorite tools

10. How can we...
- eliminate implementation time
- let data scientists use their favorite tools
...without altering your workflow

11. How do we do this?

12. How do we do this?

13. great for analysis
● Built for analysis and statistics
● Everything is tabular
● Active community; 4000+ packages

14. great for analysis
● Built for analysis and statistics
● Everything is tabular
● Active community; 4000+ packages
bad for applications
● Not web friendly
● Everything is tabular
● Slow
● A list of R grievances:
○ https://github.com/tdsmith/aRrg

15. Hooking R up to Python

16. R code

17. R code > Compile to Bytecode

18. R code > Compile to Bytecode > Execute from Python
{
“data”: {
“foo”: 100,
“bar”: 200
}
}
Incoming data for
prediction Make prediction
from Python using
compiled R

19. R code > Compile to Bytecode > Execute from Python
Returned via
REST API
Prediction sent back
to Python
webserver{
“prob”: 0.95
}

20. {}
approach
Same Python server

21. {}
approach
Plug in different scientific
environments

22. { }“prob”: 0.87
approach
Predictions sent back up
the chain and to the client

23. Result
● Ensures cross environment validation
● Extensible to other languages

24. Want to try?
yhathq.com

25. We're Hiring
info@yhathq.com
yhathq.com/jobs

26. Questions?
greg@yhathq.com
yhathq.com
@YhatHQ
blog.yhathq.com