A description of machine learning and prediction APIs followed by some real-world considerations on the deployment of predictive models and their integration in apps and businesses. These are illustrated with a churn prediction example.
In this presentation you'll find some information about Microsoft Azure Machine Learning and how it compares to PredictionIO, an open source solution for creating prediction servers. I also gave an exclusive presentation of the Machine Learning Canvas, which you must fill in before any attempt at implementing a predictive system!
Video coming soon...
9. –McKinsey & Co.
“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.”
32. The two phases of machine learning:
• TRAIN a model
• PREDICT with a model
33. The two methods of prediction APIs:
• TRAIN a model
• PREDICT with a model
34. The two methods of prediction APIs:
• model = create_model(dataset)
• predicted_output =
create_prediction(model, new_input)
35. 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
38. Phrase problem as ML task
Engineer features
Prepare data (csv)
Learn model
Make predictions
Deploy model & integrate pred
Evaluate model
Measure impact
PREDICTIONAPIS
39. • Deployment to production?
• Maintenance?
• monitor performance
• update with new data
53. • Who: SaaS company selling monthly
subscription
• Question asked: “is this customer going to
leave within 1 month?”
• Input: customer
• Output: no-churn (negative) or churn
(positive)
• Data collection: history up until 1 month ago
70. Why fill in ML canvas?
• target the right problem for your
company
• choose right algorithm,
infrastructure, or ML solution
• guide project management
• improve team communication
73. • Create value from data with ML!
• Creating and deploying models is
easy(er)!
• Good data is essential!
• Use the ML canvas!
• Go to PAPIs Connect!
74. Some real-world insights
• Models that are easier to maintain
cost less
• Need to explain predictions?
• One problem may call for another
one…