Your SlideShare is downloading. ×
Python at yhat (august 2013)
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

Python at yhat (august 2013)

391

Published on

Published in: Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
391
On Slideshare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
12
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 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?
  • 6. What goes on in the Kludge? Rewriting Code Batch Jobs PMML
  • 7. How can we... - eliminate implementation time
  • 8. How can we... - eliminate implementation time - let data scientists use their favorite tools
  • 9. How can we... - eliminate implementation time - let data scientists use their favorite tools ...without altering your workflow
  • 10. How do we do this?
  • 11. How do we do this?
  • 12. great for analysis ● Built for analysis and statistics ● Everything is tabular ● Active community; 4000+ packages
  • 13. 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
  • 14. Hooking R up to Python
  • 15. R code
  • 16. R code > Compile to Bytecode
  • 17. R code > Compile to Bytecode > Execute from Python { “data”: { “foo”: 100, “bar”: 200 } } Incoming data for prediction Make prediction from Python using compiled R
  • 18. R code > Compile to Bytecode > Execute from Python Returned via REST API Prediction sent back to Python webserver{ “prob”: 0.95 }
  • 19. {} approach Same Python server
  • 20. {} approach Plug in different scientific environments
  • 21. { }“prob”: 0.87 approach Predictions sent back up the chain and to the client
  • 22. Result ● Ensures cross environment validation ● Extensible to other languages
  • 23. Want to try? yhathq.com
  • 24. We're Hiring info@yhathq.com yhathq.com/jobs
  • 25. Questions? greg@yhathq.com yhathq.com @YhatHQ blog.yhathq.com

×