the united states postal service processed over 150 billion pieces of mail in 2013—far too much...
TRANSCRIPT
Spark the future.
May 4 – 8, 2015Chicago, IL
Azure Machine Learning MarketplaceElad ZiklikGroup ManagerAzure ML Marketplace
BRK2560
Who is this guy ?
Big question for FIFA SOCCER 2014 World Cup :
“ Who will be Paul II ?”
….and the winner is
“ Accurate predictions in 15 out of 16 games in K.O. stage
“
Cortana did not stop !
Oscars!
What is this graph?
Some #HowOldNumbers
In 4 days: • 33M users• 1.2M visitors /
h• 236M Images• 10K images /
sec• 1.7M FB
Shares
The United States Postal Service processed over 150 billion pieces of mail in 2013—far too much for efficient human sorting.
But as recently as 1997, only 10% of hand-addressed mail was successfully sorted automatically.
ML in the “real world”
The challenge in automation is enabling computers to interpret endless variation in handwriting.
The challenge in automation is enabling computers to interpret endless variation in handwriting.
By providing feedback, the Postal Service was able to train computers to accurately read human handwriting.
Today, with the help of machine learning, over 98% of all mail is successfully processed by machines.
Machine Learning - using past data to predict the future
Recommenda-tion engines
Advertising analysis
Weather forecasting for business planning
Social network analysis
IT infrastructure and web app optimization
Legal discovery and document archiving
Pricing analysisFraud detection
Churn analysis
Equipment monitoring
Location-based tracking and services
Personalized Insurance
Imagine what Machine Learning could do to your
business
IntroducingZiklikMart
Classical market basket analysis Increases cross-sell and up-sell, provides pricing opportunities Most retailers use manual business rules to offer upsells, cross
sells
Appears on almost every Amazon item page
Frequently bought together
Make machine learning accessible to every enterprise, data scientist, developer, information worker, consumer, and device anywhere in the world.
Azure Machine Learning Vision
Make machine learning accessible to every enterprise, data scientist, developer, information worker, consumer, and device anywhere in the world.
Azure ML Vision - Marketplace
ML APIs
Marketplace
ML Operationalization
ML Studio
ML Algorithms
Azure ML Marketplace
ProducerConsumer
JJ Foods & ML Frequently Bought Together
Customers Quick service restaurants Retailing Educatering Pubs and hotels Event catering
Year 2014 8 sites 60,000 customers 4,500 unique products 1 million sales orders £185 million turnover
JJ Food Service
2 recommendation /personalization scenarios Item-specific recommendations Checkout-specific
recommendations
6% of items added to cart come from Azure ML models
5% Conversion rate at checkout
JJ Food Service
Product Recommendati
ons
Basket Analysis
Customer Churn
Prediction
Text Analytics
Anomaly Detection
Azure Machine Learning APIs
Customer ChurnPrediction
Churn Interactive Experience
For every business, some customer churn – leave
What if we can tell ahead of time which customers are going to leave?
Machine Learning Algorithm can “learn” from previous history transaction patterns
Use the trained model to make predictions
Target customers most likely to leave
Customer Churn Prediction – How?
JJ Foods & Customer Churn Prediction
Customers Quick service restaurants Retailing Educatering Pubs and hotels Event catering
Year 2014 8 sites 60,000 customers 4,500 unique products 1 million sales orders £185 million turnover
JJ Food Service
Top 20% of churn predictions are over 3x better than random pick
Out of 200 predicted using our system ~45% retained Past experience without our
system was ~17% retention rate
JJ Food Service
Text Analytics ServiceAnalyzes unstructured text.Product reviews, support tickets, emails, etc.
Sentiment AnalysisHow do your customers feel about your brand or products?
Key Phrase ExtractionWhat are your customers talking about?
Sentiment analysis
Key phrase extraction
“It was a wonderful hotel, with unique décor and friendly staff”
“It was a wonderful hotel, with unique décor and friendly staff”Interactive Experience
Text Analytics Service Use Cases
Sentiment of Brand/Product
Customer Support Triage
User ReviewsTopic Extraction
Jul-13 Aug-13
Sep-13
Oct-13
Nov-13
Dec-13
Jan-14 Feb-14
Mar-14
Apr-14
May-14
Jun-14 Jul-14 Aug-14
Sep-14
Oct-14
Nov-14
Dec-14
Jan-15 Feb-15
0.65
0.66
0.67
0.68
0.69
0.7
0.71
0.72
0.73
Sentiment
Sentim
ent
Overall Sentiment over Time
Jul-13 Aug-13
Sep-13
Oct-13
Nov-13
Dec-13
Jan-14 Feb-14
Mar-14
Apr-14
May-14
Jun-14 Jul-14 Aug-14
Sep-14
Oct-14
Nov-14
Dec-14
Jan-15 Feb-15
0.65
0.66
0.67
0.68
0.69
0.7
0.71
0.72
0.73
Sentiment
Sentim
ent
Overall Sentiment over Time
Oct 2013
Oct 2014
Top Key Phrases with Positive Sentiment
Negative21%
Neutral15%
Positive64%
Negative
Neutral
Positive
ScorePositive sentiment: above 0.65Negative sentiment: below 0.4Neutral sentiment: between 0.4 & 0.65
% of Users by Sentiment
Fans
UserAvg Sentiment
Score# of Text Pieces
user1723 0.390934 8
user228 0.323264 6
user1250 0.392574 6
user955 0.245724 5
user229 0.28559 5
user518 0.328739 5
user1532 0.398888 5
user4963 0.179529 4
user2541 0.212446 4
user312 0.221297 4
UserAvg Sentiment
Score# of Text Pieces
user77 0.758117 69
user23 0.833552 58
user243 0.716238 53
user396 0.974862 40
user1403 0.716291 38
user215 0.929712 35
user862 0.838113 27
user40 0.972512 27
user780 0.730805 26
user268 0.746828 22
Critics
Potential Evangelists Fix your relationship
Anomaly Detection
SLA 99.99%
Behind the scenes of the #HowOldRobot – Introducing Project Oxford
A portfolio of REST APIs and SDKs which enable developers to write applications which understand the content within the rapidly growing set of multimedia data
Project Oxford’s API services will help you understand and interact with audio, text, image, and video
Understand the data around your application
Microsoft Project Oxford Services
PROJECT OXFORD
Speech APIs LUIS (Language Understanding Intelligent Service)
Vision APIs Face APIs
OCR
Speech Recognition
Text to Speech
Speech Intent Recognition
Determine Entities
Behind #HowOldRobot - Microsoft Project Oxford
PROJECT OXFORD
Face Grouping
Face Identification
Face DetectionAnalyze Image
Generate Thumbnail
Improve Models
Detect Intent
Face APIsVision APIsLUIS(Language Understanding Intelligent Service)
Speech APIs
Face APIsDetectionVerificationGroupingIdentification
Face API – DetectionDetection Result:JSON:[ { "faceRectangle": { "width": 109, "height": 109, "left": 62, "top": 62 }, "attributes": { "age": 31, "gender": "male", "headPose": { "roll": "2.9", "yaw": "-1.3", "pitch": "0.0" } "faceLandmarks": { "pupilLeft": { "x": "93.6", "y": "88.2" }, "pupilRight": { "x": "138.4", "y": "91.7" }, ...
INPUTIMAGE
FACIALRECTANGLE + LANDMARKS
DETECTION ATTRIBUTES
Verification Result:JSON:[
{"isIdentical":false,"confidence":0.01
}]
Face API – Verification
Given two faces, determine whether they are the same person
CLUSTERED BY DETECTED PEOPLE
Face API – Grouping
He is Chao Wang.
Face API – Identify
NEW INPUTIMAGE
IDENTIFY
Natalie Huber
GROUP PERSON OBJECTS
COLLEAGUES
RECOGNITION
Chao Wang
Vision APIsAnalyze an ImageOCRGet Thumbnail
Understand content and features within an image
Analyze Image Service
Analyze Image – Example
Type of Image:
Clip Art Type 0 Non-clipart
Line Drawing Type 0 Non-Line Drawing
Black & White Image False
Content of Image:
Categories [{ “name”: “people_swimming”, “score”: 0.099609375 }]
Adult Content False
Adult Score 0.18533889949321747
Faces [{ “age”: 27, “gender”: “Male”, “faceRectangle”: {“left”: 472, “top”: 258, “width”: 199, “height”: 199}}]
Image Colors:
Dominant Color Background White
Dominant Color Foreground Grey
Dominant Colors White
Accent Color
Detect and recognize words within a photo
OCR Service
OCR – Example
JSON:{
"language": "en","orientation": "Up","regions": [
{
"boundingBox": "41,77,918,440","lines": [
{
"boundingBox": "41,77,723,89",
"words": [
{
"boundingBox": "41,102,225,64",
"text": "LIFE"
},
{
"boundingBox": "356,89,94,62",
"text": "IS"
},
{
"boundingBox": "539,77,225,64",
"text": "LIKE"
}. . .
TEXT:LIFE IS LIKERIDING A BICYCLETO KEEP YOUR BALANCEYOU MUST KEEP MOVING
Stay tuned to our Azure ML blog https://aka.ms/mlblog
Publishers welcome ! Be a part of this new data science economy. Contact us at [email protected]
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