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.

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.

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 analysis Fraud detection Churn analysis Equipment monitoring Location-based tracking and services Personalized Insurance Imagine what Machine Learning could do to your business

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

 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

Customer Churn Prediction  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?

 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 Service Analyzes unstructured text. Product reviews, support tickets, s, etc. Sentiment Analysis How do your customers feel about your brand or products? Key Phrase Extraction What are your customers talking about? Sentiment analysis Key phrase extraction “It was a wonderful hotel, with unique décor and friendly staff”

Text Analytics Service Use Cases Sentiment of Brand/Product Customer Support Triage User Reviews Topic Extraction

Overall Sentiment over Time

Oct 2013 Oct 2014 Top Key Phrases with Positive Sentiment

Score Positive sentiment: above 0.65 Negative sentiment: below 0.4 Neutral sentiment: between 0.4 & 0.65 % of Users by Sentiment

Fans UserAvg Sentiment Score# of Text Pieces user user user user user user user user user user UserAvg Sentiment Score# of Text Pieces user user user user user user user user user user Critics Potential Evangelists Fix your relationship

Anomaly Detection

Detection 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" },...

Verification Result: JSON: [ { "isIdentical":false, "confidence":0.01 } ]

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 LIKE RIDING A BICYCLE TO KEEP YOUR BALANCE YOU MUST KEEP MOVING

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