Yhat presentation at PyData Boston 2013. Predictive Models for Production Apps with Yhat. Building a beer recommender with Python and Yhat.

1. Predictive Models for
Production Applications
July 2013

2. Why building analytical apps is hard
Overcoming the challenge
Case study: building a beer recommender

3. If you double the number of
experiments you do per year, you're
going to double your inventiveness.
“
”- Jeff Bezos

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

5. 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.

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

7. It's hard to incorporate
analytical work into
day-to-day operations

8. We know finding a data scientist tough.
http://drewconway.com/

9. Building applications
from their insights is tougher.

10. "cool. what do we do now?" scenarios
http://scikit-learn.org/stable/auto_examples/index.html

11. Product Page Search Results
Order Confirmation / Checkout Page
Reduce QA time for classifying purchases made online

12. How to go from prototype to product?

13. How do companies
address this problem?

14. Rewriting Code
Common Approaches Challenge

15. Rewriting Code
Common Approaches
Cross-environment validation
Challenge

16. Rewriting Code
Batch Jobs
Common Approaches
Cross-environment validation
Challenge

17. Rewriting Code
Batch Jobs
Common Approaches
Cross-environment validation
High maintenance and config
Challenge

18. Rewriting Code
Batch Jobs
PMML
Common Approaches
Cross-environment validation
High maintenance and config
Challenge

19. Rewriting Code
Batch Jobs
PMML
Common Approaches
Cross-environment validation
High maintenance and config
Limited to certain libraries, Still rewriting
Challenge

20. Rewriting Code
Batch Jobs
PMML
Common Approaches
Cross-environment validation
High maintenance and config
Limited to certain libraries, Still rewriting
Challenge
More people, more tools, more time to market.

21. Can we build and bring
to market smarter
applications faster?

22. Rewriting Code
Batch Jobs
PMML
Common Approaches
Cross-environment validation
High maintenance and config
Limited to certain libraries, Still rewriting
Challenge
A platform for running predictive models in
production applications.

23. Key Tenets

24. 1. Work with the tools you already know
Key Tenets

25. 1. Work with the tools you already know
2. Iterate quickly
Key Tenets

26. 1. Work with the tools you already know
2. Iterate quickly
3. Low touch
Key Tenets

27. 1. Work with the tools you already know
2. Iterate quickly
3. Low touch
4. No rewriting code
Key Tenets

28. demo

29. A Beer Recommender in Python

30. What beer should I drink?

31. Tell us a beer you like

33. We'll tell you some
other beers you'll like

34. 1. Import the data
2. Find common reviewers
3. Calculate review distance
4. Rank beers
Plan

35. 1. Import the data
2. Find common reviewers
3. Calculate review distance
4. Rank beers
Plan

36. The Dataset

37. The Dataset

38. The Dataset

39. 1. Import the data
2. Find common reviewers
3. Calculate review distance
4. Rank beers
Plan

40. Find common reviewers

41. 1. Import the data
2. Find common reviewers
3. Calculate distance
4. Rank beers
Plan

42. Comparing 2 Similar Beers
vs

43. Dale's Pale Ale and Fat Tire Amber Ale

44. Dale's Pale Ale and Fat Tire Amber Ale
"Perfect Agreement"

45. Dale's Pale Ale and Fat Tire Amber Ale

46. Dale's Pale Ale and Fat Tire Amber Ale

47. Dale's Pale Ale and Fat Tire Amber Ale

48. Dale's Pale Ale and Fat Tire Amber Ale
Similar reviews

49. Comparing 2 Dissimilar Beers
vs

50. Dale's Pale Ale and Fat Tire Amber Ale

51. Michelob Ultra and Fat Tire Amber Ale

52. Measuring distance

53. Measuring distance
...yes, there are other ways to do this.

54. Calculating Distance

55. Distance Implementation

56. Calculate the Distance
● Generate all beer pairs
● Calculate distance between each pair

57. 1. Import the data
2. Find common reviewers
3. Calculate review distance
4. Rank results
Plan

58. So if I like Coors Light, what other
beers might I like?

59. shipping your work
with

60. Make analytical routines available to other apps
{ "beer": "Coors Light",
"weights": [3, 2, 0, 1]}
[
["Bud Light", 9.2],
["Budweiser", 12.2],
["Sierra Nevada", 21.2],
]

61. 1. Pre-processing & transformations

62. 2. Prediction & post-processing

63. Use the same code you wrote
during exploration and modeling.
2. Prediction & post-processing

64. We're ready to deploy

65. Analytical routine is now ready to be
deployed Go-to-market with as little overhead as possible

66. Deploy
Pass objects you'd like included in
your model as named arguments

67. Specify User Defined
Functions you want to
include in your project
Deploy

68. Pass the name of your
model and your BaseModel
object to the deploy
function
Deploy

69. Execute deploy to host
your model on Yhat
Deploy

70. Make predictions in a
production app

71. pydata-beer.herokuapp.com
https://github.com/yhat/Beer-Rec-Flask

72. Data/Code Bundle
Webapp: https://github.com/yhat/Beer-Rec-Flask
IPython Notebook: http://bit.ly/1bkCTHz
Dataset: http://bit.ly/14Wl64k

73. Want to try?
yhathq.com

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

75. Questions?
greg@yhathq.com
austin@yhathq.com
yhathq.com
@YhatHQ
blog.yhathq.com

76. Appendix

77. Learn by iteration from the context of real-
world business applications.

78. Deployment
Execute the deploy
function to host your
model on Yhat
Pass the name of your
model and your BaseModel
object to the deploy
function
Pass objects you'd like included in
your model as named arguments
Specify User Defined
Functions you want to
include in your project