Data Analytics in Azure Cloud

3. Microsoft Technology Centers The opportunity is bigger than you may think $1.6Tdata dividend available to businesses that embrace data over the next four years speed More people New analytics Diverse data Data source: Microsoft & IDC, April 2014

4. Microsoft Technology Centers The world of data has changed

5. Microsoft Technology Centers Advanced Analytics Beyond business intelligence Descriptive Analytics Diagnostic Analytics Predictive Analytics Prescriptive Analytics

6. Microsoft Technology Centers Powerful, familiar analytical tools for everyone

7. Microsoft Technology Centers Internal & external Dashboards Ask Mobile Information management Prediction Data ReportsApplications Orchestration Relational  StreamingNon-relational Query

8. Microsoft Technology Centers What is the Internet of Things? Connectivity Data AnalyticsThings

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10. Microsoft Technology Centers Canonical IoT Infrastructure

11. Microsoft Technology Centers

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13. Microsoft Technology Centers 13 Prediction is very difficult, especially about the future. Niels Bohr (1885-1962)

14. Microsoft Technology Centers What is Machine Learning ? • Arthur Samuel (1959). Machine Learning: Field of study that gives computers ability to learn without being explicitly programmed • Tom Mitchell (1998). Well-posed Learning Problem: A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E. • Chris Bishop (2006). The field of pattern recognition is concerned with automatic discovery of regularities in data through the use of computer algorithms and with the use of these regularities to take actions such as classifying the data to different categories. 14

15. Microsoft Technology Centers When is Machine Learning used? • A pattern exists • We cannot pin it down mathematically • We have data on it • Learn it when you can’t code it • Learn it when you can’t scale it • Learn it when you have to adapt/personalize 15

16. Microsoft Technology Centers Examples of Machine Learning applications • Hospital readmissions. • Stock price prediction. • Character recognition. • Health risk factors identification • Genomics • Smart buildings • Predictive maintenance • Sales forecasting • Demand forecasting • Fraud detection • Credit risk management • User segmentation • Personalized offers • Product recommendations 16

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19. Microsoft Technology Centers Ready to build a predictive model? • How do I translate my business question to a machine learning task? Or task? • What data do I need to answer my question? • Is my data clean? • How much data do I need? • Which model to choose? Are neural networks better than random forests for my problem? • Are my features right ones? • Which features have the most predictive value? • My data has hundreds of attributes. What do I do? • How do I measure my model’s performance? • When do I know I am done? • My model performs perfect on my training data but my client tells me its predictions are terrible? • How do I use my model from a smartphone app? • ……… 19

20. Microsoft Technology Centers Establishing Data Science Practice can be Challenging Specialized skills Complex analytics Long time to insight Model governance

21. Microsoft Technology Centers Azure ML Streamlines Data Science Process Marketplace Collaboration Scale out Ease of deployment

22. Microsoft Technology Centers 22 “Nothing is more practical than a good theory” Vladimir Vapnik

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Data Analytics in Azure Cloud

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