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Predicting the Future With Microsoft Bing


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The next generation of data scientists will be asked to build predictive models that can extract inferences from very large datasets which are unobservable at the surface, even to the best domain experts. Microsoft has access to some truly large data sets, web and search data from the Bing search engine and social data through collaborations with Twitter. In this talk, we show you how a small team of data scientists used this data to build the Bing Predicts engine — a collection of machine learnt predictive models that is beating industry experts at predicting the outcome of events like the Super Bowl, the Oscars, elections and referendums and even breakthroughs in health sciences. The talk will also give a preview of how organizations can adopt a big data mindset to generate and experiment with large data sets and to make amazing predictions using their own data.

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Predicting the Future With Microsoft Bing

  1. 1. Predicting the Future: Surprising revelations from truly Big Data Pushpraj Shukla
  2. 2. Bing Predicts
  3. 3. Knowledge Understanding what the data means Information Indexing, aggregating, categorizing the data Data Unstructured and unorganized
  4. 4. What can we predict?
  5. 5. *
  6. 6. *Data from
  7. 7. Did we get lucky?
  8. 8. But wait, there’s more
  9. 9. Data science skepticism “The Unreasonable Effectiveness of Mathematics in the Natural Sciences”, Eugene Wigner, 1960. • Isn’t it remarkable how well we can predict physical phenomena using simple equations like F = ma or PV = nRT? “The Unreasonable Effectiveness of Data”, Alon Halevy, Peter Norvig, & Fernando Pereira, 2009. • Isn’t it remarkable how well we can predict human phenomena using simple statistical models and massive quantities of training data?
  10. 10. Task: Classify events into "tau tau decay of a Higgs boson" versus "background
  11. 11. Data Science and Machine Learning @Microsoft ‘Big’ Tasks – Predicting human behavior and preferences, Speech, Translation, Image and Video recognition ‘Big’ Data = SCALE - Huge data. More data = better accuracy - Huge models. More features = better accuracy - Huge human feedback ‘Big’ Infrastructure and Experimental Ecosystems
  12. 12. Machine learning for everyone Big Tasks: • Be curios, make bold hypotheses • Ask challenging questions • Disrupt polls and traditional data collection Big Data • Create data: high volume public/private data streams • Join multiple data sources: Weather, Public Health, Traffic, Crime etc. • Utilize the Crowd: Mechanical Turk, Kaggle Smart Infrastructure • Re-use solutions: public APIs in Text Analytics, Speech,Image • Cloud-based ML solutions
  13. 13. Collaboration and learning Existing templates and APIs for: Stream Analytics Anomaly Detection Sentiment Analysis Churn prediction Sales Forecasting Face recognition Speech Recognition .. And many more Azure ML : ML Studio pictured below
  14. 14. Questions? Some References: Bing Predicts: AzureML : Reach me at