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![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](https://image.slidesharecdn.com/apidays-150507152940-lva1-app6891/75/Using-predictive-APIs-to-create-smarter-apps-24-2048.jpg)
![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](https://image.slidesharecdn.com/apidays-150507152940-lva1-app6891/75/Using-predictive-APIs-to-create-smarter-apps-25-2048.jpg)
Uploaded byLouis Dorard
Using predictive APIs to create smarter apps
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.
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Using predictive APIs to create smarter apps
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- 6. –Mike Gualtieri, PrincipalAnalyst at Forrester “Predictive apps are the next big thing in app development.”
- 7. –Waqar Hasan, VISA “Predictiveis the ‘killer app’ for big data.”
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- 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.”
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- 15. “Which type ofemail is this? — Spam/Ham” -> Classification
- 16. “Which type ofemail is this? — Spam/Ham” I O
- 18. • Need examplesof inputs AND outputs • Need enough examples
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- 21. The two phasesof machine learning: • TRAIN a model • PREDICT with a model
- 22. The two methodsof predictive APIs: • TRAIN a model • PREDICT with a model
- 23. The two methodsof predictive APIs: • model = create_model(dataset) • predicted_output = create_prediction(model, new_input)
- 24. from bigml.api importBigML # 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 importBigML # 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
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- 30. “Is this emailimportant? — Yes/No”
- 31. Generic Predictive APIs: •Google Prediction API • BigML • PredicSis • WolframCloud • Amazon
- 32. AMAZON GOOGLE PREDICSISBIGML 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
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- 34. “Which project doesthis document relate to? — A/B/C”
- 35. Text classification APIs: •uClassify.com • MonkeyLearn.com • Cortical.io
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- 37. “Is this customergoing to leave next month? — Yes/No”
- 38. “Is this transactionfraudulent? — 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
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- 41. “What is thesentiment of this tweet? — Positive/Neutral/Negative”
- 42. “Is this emailspam? — Yes/No”
- 43. Fixed-model Predictive APIs: •Text: Datumbox.com, Semantria.com • Vision: Indico, AlchemyAPI.com • Siri-like: maluuba.com
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- 55. • “Open sourceprediction server” • Based on Spark • Expose predictive models as (scalable & robust) APIs
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- 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})
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- 61. • Increase e-commercerevenue with recos • Reduce attrition (“churn”) • Automate decision making • Predict real-estate prices w. open data • Predict crowds (SNCF & La Poste) • …
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