Using predictive APIs to create smarter apps

•

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This document discusses predictive APIs and machine learning. It covers the two phases of machine learning: training a model and predicting with a model. It also discusses the different types of predictive APIs based on their level of abstraction, including generic, text classification, problem-specific, and fixed model APIs. It provides examples of APIs for each type. The document also discusses customizing predictive APIs and introduces PredictionIO as an open source predictive serving engine.

Technology

–Mike Gualtieri, Principal Analyst at Forrester
“Predictive apps are
the next big thing
in app development.”

–Waqar Hasan, VISA
“Predictive is the ‘killer app’ for
big data.”

–McKinsey & Co. (2011)
“A signiﬁcant constraint on
realizing value from big data
will be a shortage of talent,
particularly of people with
deep expertise in statistics and
machine learning.”

“Which type of email is this?
— Spam/Ham”
 
-> Classiﬁcation

“Which type of email is this?
— Spam/Ham”
 
I
O

• Need examples of inputs AND outputs
• Need enough examples

The two phases of machine learning:
• TRAIN a model
• PREDICT with a model

The two methods of predictive APIs:
• TRAIN a model
• PREDICT with a model

The two methods of predictive APIs:
• model = create_model(dataset)
• predicted_output =
create_prediction(model, new_input)

from bigml.api import BigML 
# create a model
api = BigML()
source =
api.create_source('training_data.csv')
dataset = api.create_dataset(source)
model = api.create_model(dataset) 
# make a prediction
prediction = api.create_prediction(model,
new_input)
print "Predicted output value:
",prediction['object']['output']
http://bit.ly/bigml_wakari

Different needs,
different Predictive APIs

ABSTRACTION
Algorithmic
Generic
Generic for text
Problem-speciﬁc
Fixed-model

Generic Predictive APIs:
• Google Prediction API
• BigML
• PredicSis
• WolframCloud
• Amazon

AMAZON GOOGLE PREDICSIS BIGML
ACCURACY 0.862 0.743 0.858 0.790
TRAINING
TIME
135s 76s 17s 5s
TEST TIME 188s 369s 5s 1s
louisdorard.com/blog/machine-learning-apis-comparison

“Which project does this document relate to?
— A/B/C”

Text classiﬁcation APIs:
• uClassify.com
• MonkeyLearn.com
• Cortical.io

“Is this customer going to leave next month?
— Yes/No”

“Is this transaction fraudulent?
— Yes/No”

Problem-speciﬁc Predictive APIs:
• Churn: ChurnSpotter.io, Framed.io
• Fraud detection: SiftScience.com
• Lead scoring: Infer.com
• Personal assistant (Siri): Wit.ai

“What is the sentiment of this tweet?
— Positive/Neutral/Negative”

Fixed-model Predictive APIs:
• Text: Datumbox.com, Semantria.com
• Vision: Indico, AlchemyAPI.com
• Siri-like: maluuba.com

CUSTOMIZATION
Algorithmic
Generic
Generic for text
Problem-speciﬁc
Fixed-model

• “Open source prediction server”
• Based on Spark
• Expose predictive models as (scalable
& robust) APIs

• Send (new) data
• Send prediction queries

$cli = predictionio.EventClient("<my_app_id>") cli.record_user_action_on_item("view","John","Item") eng = predictionio.EngineClient("<my_engine_url>") rec = eng.send_query({"uid":"John","n":5})$

• Increase e-commerce revenue with recos
• Reduce attrition (“churn”)
• Automate decision making
• Predict real-estate prices w. open data
• Predict crowds (SNCF & La Poste)
• …

papis.io/connect
(discount code: APIDAYS)

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Using predictive APIs to create smarter apps

1. @louisdorard #apidays

5. PredictiveAPIs

6. –Mike Gualtieri, Principal Analyst at Forrester “Predictive apps are the next big thing in app development.”

7. –Waqar Hasan, VISA “Predictive is the ‘killer app’ for big data.”

8. 1. Machine Learning 2. Data

9. BUT

10. –McKinsey & Co. (2011) “A signiﬁcant constraint on realizing value from big data will be a shortage of talent, particularly of people with deep expertise in statistics and machine learning.”

11.

12.

13. What the @#?~%  is ML?

14.

15. “Which type of email is this? — Spam/Ham”   -> Classiﬁcation

16. “Which type of email is this? — Spam/Ham”   I O

17.

18. • Need examples of inputs AND outputs • Need enough examples

19. ??

20. Predictive APIs

21. The two phases of machine learning: • TRAIN a model • PREDICT with a model

22. The two methods of predictive APIs: • TRAIN a model • PREDICT with a model

23. The two methods of predictive APIs: • model = create_model(dataset) • predicted_output = create_prediction(model, new_input)

24. from bigml.api import BigML  # create a model api = BigML() source = api.create_source('training_data.csv') dataset = api.create_dataset(source) model = api.create_model(dataset)  # make a prediction prediction = api.create_prediction(model, new_input) print "Predicted output value: ",prediction['object']['output'] http://bit.ly/bigml_wakari

25. from bigml.api import BigML  # create a model api = BigML() source = api.create_source('training_data.csv') dataset = api.create_dataset(source) model = api.create_model(dataset)  # make a prediction prediction = api.create_prediction(model, new_input) print "Predicted output value: ",prediction['object']['output'] http://bit.ly/bigml_wakari

26.

27. Different needs, different Predictive APIs

28. ABSTRACTION Algorithmic Generic Generic for text Problem-speciﬁc Fixed-model

29. ABSTRACTION Algorithmic Generic Generic for text Problem-speciﬁc Fixed-model

30. “Is this email important? — Yes/No”

31. Generic Predictive APIs: • Google Prediction API • BigML • PredicSis • WolframCloud • Amazon

32. AMAZON GOOGLE PREDICSIS BIGML ACCURACY 0.862 0.743 0.858 0.790 TRAINING TIME 135s 76s 17s 5s TEST TIME 188s 369s 5s 1s louisdorard.com/blog/machine-learning-apis-comparison

33. ABSTRACTION Algorithmic Generic Generic for text Problem-speciﬁc Fixed-model

34. “Which project does this document relate to? — A/B/C”

35. Text classiﬁcation APIs: • uClassify.com • MonkeyLearn.com • Cortical.io

36. ABSTRACTION Algorithmic Generic Generic for text Problem-speciﬁc Fixed-model

37. “Is this customer going to leave next month? — Yes/No”

38. “Is this transaction fraudulent? — Yes/No”

39. Problem-speciﬁc Predictive APIs: • Churn: ChurnSpotter.io, Framed.io • Fraud detection: SiftScience.com • Lead scoring: Infer.com • Personal assistant (Siri): Wit.ai

40. ABSTRACTION Algorithmic Generic Generic for text Problem-speciﬁc Fixed-model

41. “What is the sentiment of this tweet? — Positive/Neutral/Negative”

42. “Is this email spam? — Yes/No”

43. Fixed-model Predictive APIs: • Text: Datumbox.com, Semantria.com • Vision: Indico, AlchemyAPI.com • Siri-like: maluuba.com

44. ABSTRACTION Algorithmic Generic Generic for text Problem-speciﬁc Fixed-model

45. CUSTOMIZATION Algorithmic Generic Generic for text Problem-speciﬁc Fixed-model

46. Own ML  algorithm?

47. Own data processing  algorithm?

48. Data processing APIﬁcation

49.

50.

51.

52. MLlib

53.

54.

55. • “Open source prediction server” • Based on Spark • Expose predictive models as (scalable & robust) APIs

56. = PAPI+

57. • Send (new) data • Send prediction queries

58. cli = predictionio.EventClient("<my_app_id>") cli.record_user_action_on_item("view","John","Item") eng = predictionio.EngineClient("<my_engine_url>") rec = eng.send_query({"uid":"John","n":5})

59.

60. Use cases

61. • Increase e-commerce revenue with recos • Reduce attrition (“churn”) • Automate decision making • Predict real-estate prices w. open data • Predict crowds (SNCF & La Poste) • …

62.

63. papis.io/connect (discount code: APIDAYS)

Using predictive APIs to create smarter apps

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