Des exemples de use cases dont vous pourrez vous inspirer, et de plateformes de ML-as-a-Service pour vous faciliter le human learning du machine learning, l'expérimentation, et le déploiement en production!

1. Machine Learning: je m’y mets
demain!
@louisdorard
#TTFX - March 31, 2016

2. AI is everywhere

4. Amazon for David Jones (@d_jones, see source)

5. Amazon for David Jones (@d_jones, see source)

11. Lars Trieloff
@trieloff
(see source)

12. @louisdorard

13. ChurnSpotter.io

18. • Startups pitch
• AI asks questions live to each startup
• AI assigns score
• Startup with highest score wins 100000 €
18
AI Startup Battle at PAPIs.io

20. Preseries

21. How does it work?

22. Data + Machine Learning

24. Bedrooms Bathrooms Surface (foot²) Year built Type Price ($)
3 1 860 1950 house 565,000
3 1 1012 1951 house
2 1.5 968 1976 townhouse 447,000
4 1315 1950 house 648,000
3 2 1599 1964 house
3 2 987 1951 townhouse 790,000
1 1 530 2007 condo 122,000
4 2 1574 1964 house 835,000
4 2001 house 855,000
3 2.5 1472 2005 house
4 3.5 1714 2005 townhouse
2 2 1113 1999 condo
1 769 1999 condo 315,000

25. Bedrooms Bathrooms Surface (foot²) Year built Type Price ($)
3 1 860 1950 house 565,000
3 1 1012 1951 house
2 1.5 968 1976 townhouse 447,000
4 1315 1950 house 648,000
3 2 1599 1964 house
3 2 987 1951 townhouse 790,000
1 1 530 2007 condo 122,000
4 2 1574 1964 house 835,000
4 2001 house 855,000
3 2.5 1472 2005 house
4 3.5 1714 2005 townhouse
2 2 1113 1999 condo
1 769 1999 condo 315,000

27. Bedrooms Bathrooms Surface (foot²) Year built Type Price ($)
3 1 860 1950 house 565,000
3 1 1012 1951 house
2 1.5 968 1976 townhouse 447,000
4 1315 1950 house 648,000
3 2 1599 1964 house
3 2 987 1951 townhouse 790,000
1 1 530 2007 condo 122,000
4 2 1574 1964 house 835,000
4 2001 house 855,000
3 2.5 1472 2005 house
4 3.5 1714 2005 townhouse
2 2 1113 1999 condo
1 769 1999 condo 315,000

28. ML is a set of AI techniques where
“intelligence” is built from
examples

30. 30
Use cases
• Real-estate
• Spam filtering
• City bikes
• Startup competition
• Reduce churn
• Optimize pricing
• Anticipate demand
property price
email spam indicator
location, context #bikes
startup success indicator
customer churn indicator
product, price #sales
product, store, date #sales
Zillow
Gmail
V3 predict
Preseries
ChurnSpotter
Amazon
Blue Yonder
RULES

31. Making Machine Learning accessible
with cloud platforms

33. HTML / CSS / JavaScript

34. HTML / CSS / JavaScript

35. squarespace.com

38. The two phases of ML
• TRAIN a model
• PREDICT with a model
38
Machine Learning APIs

39. The two methods of ML Application Programming Interfaces
(here in Python)
• model = create_model(‘training.csv’)
• predicted_output, confidence =
create_prediction(model, new_input)
39
Machine Learning APIs

40. The two methods of ML Application Programming Interfaces
(here in Python)
• model = create_model(‘training.csv’)
• predicted_output, confidence =
create_prediction(model, new_input)
40
Machine Learning APIs

41. Example request to BigML API
$ curl https://bigml.io/dev/model?$BIGML_AUTH
-X POST
-H "content-type: application/json"
-d '{"dataset": "dataset/50ca447b3b56356ae0000029"}'

43. • Classification problem
• Features:
• Text of email
• Sender in address book?
• How often do I reply?
• How quickly do I reply?
• Demo
43
Priority detection

45. • VM with Jupyter notebooks (Python & Bash)
• API wrappers preinstalled: BigML & Google Pred
• Notebook for easy setup of credentials
• Scikit-learn and Pandas preinstalled
• Open source VM provisioning script & notebooks
• Search public Snaps on terminal.com:“machine learning”
45
Getting started

46. Making Machine Learning easier

47. How was it before?
from sklearn import svm
model = svm.SVC(gamma=0.001, C=100.)
from sklearn import datasets
digits = datasets.load_digits()
model.fit(digits.data[:-1], digits.target[:-1])
model.predict(digits.data[-1])

48. How was it before?
from sklearn import svm
model = svm.SVC(gamma=0.001, C=100.)
from sklearn import datasets
digits = datasets.load_digits()
model.fit(digits.data[:-1], digits.target[:-1])
model.predict(digits.data[-1])
WAT?

49. http://oscar.sensout.com

50. • Spearmint:“Bayesian optimization”for tuning parameters →
Whetlab → Twitter
• Auto-sklearn:“automated machine learning toolkit and drop-
in replacement for a scikit-learn estimator”
50
Open Source AutoML libraries

51. Scikit
from sklearn import svm
model = svm.SVC(gamma=0.001, C=100.)
from sklearn import datasets
digits = datasets.load_digits()
model.fit(digits.data[:-1], digits.target[:-1])
model.predict(digits.data[-1])

52. Scikit
from sklearn import svm
model = svm.SVC(gamma=0.001, C=100.)
from sklearn import datasets
digits = datasets.load_digits()
model.fit(digits.data[:-1], digits.target[:-1])
model.predict(digits.data[-1])

53. AutoML Scikit
import autosklearn
model = autosklearn.AutoSklearnClassifier()
from sklearn import datasets
digits = datasets.load_digits()
model.fit(digits.data[:-1], digits.target[:-1])
model.predict(digits.data[-1])

54. • Algorithm selection… AutoML
• Scaling… Azure ML or Yhat (Greg at PAPIs Connect)
• “Automating ML workflows: a report from the trenches”—
Jose A. Ortega Ruiz
54
Automatization

55. Making Deep Learning accessible

56. • Classification problem
• Input is an image = pixel values
56
Image categorization
pixel1 pixel2 pixel3 animal?
102 0 255 Yes
35 41 209 No
… … … …

57. • Neural network:
• Layers
• Neurons of one layer connected to
neurons of next layer
• Each neuron receives signals from
previous layer and sends new signal to
next layer
• New signal based on linear combination
of signals received
• “Deep”-> more than 3 layers
57
Deep Learning

58. 58
Deep Learning for animal detection

59. 59
Deep Learning for animal detection
pixel1
pixel2
pixel3
cat
dog
1st layer
value=(102, 0, 255)
Last layer
value=(0.1, 0.7, 0.4)
Output
value=(0.8, 0.3) => there’s
probably a cat!

60. 60
Deep Learning for animal detection
pixel1
pixel2
pixel3
cat
dog
1st layer
value=(4, 166, 23)
Last layer
value=(0.1, 0.7, 0.4)
Output
value=(0.1, 0.2) => probably no
animal here

61. pixel1
pixel2
pixel3
cat
dog
1st layer
value=(102, 0, 255)
Output
value=(0.8, 0.3) => there’s
probably a cat!
Last layer
value=(0.1, 0.7, 0.4)

62. 62
Deep Learning for animal detection
pixel1 pixel2 pixel3 animal?
102 0 255 Yes
35 41 209 No
… … … …
• Replace images with“smart”representation given by last layer
neuron1 neuron2 neuron3 animal?
0.1 0.2 0.5 Yes
0.8 0.3 0.8 No
… … … …

63. • Prochain meetup:
• Développer une application prédictive
(Hors-série débutants)
• Mardi 12 Avril à 19h - Le Node
• Workshop:
• Operational Machine Learning with open source and cloud platforms
• Samedi 23 Avril - sera annoncé sur le Meetup!
63
Prochains événements ML à Bordeaux

64. Machine Learning: je m’y mets
le 12 et le 23 Avril!
meetup.com/Bordeaux-Machine-Learning-Meetup/

65. meetup.com/Bordeaux-Machine-Learning-Meetup/
@louisdorard