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Condense Fact from the Vapor of Nuance

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Condense Fact from the Vapor of Nuance

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Presenter: Michael Conover, LinkedIn

Interesting, impactful problems are rarely easy to define, model, or evaluate - in fact, the very newness of a problem often suggests a scarcity of data and structured thinking about viable approaches. In this talk, we'll work through some of the challenges faced by LinkedIn's machine learning research teams in building and shipping intelligent systems that make sense of the world's economy. From creating novel training data and productionizing models with complex structure to evangelizing and evaluating the results of unsupervised algorithms, this talk will examine real-world case studies describing some of the hardest and most interesting challenges faced by one of the world's largest technology companies.

Presenter: Michael Conover, LinkedIn

Interesting, impactful problems are rarely easy to define, model, or evaluate - in fact, the very newness of a problem often suggests a scarcity of data and structured thinking about viable approaches. In this talk, we'll work through some of the challenges faced by LinkedIn's machine learning research teams in building and shipping intelligent systems that make sense of the world's economy. From creating novel training data and productionizing models with complex structure to evangelizing and evaluating the results of unsupervised algorithms, this talk will examine real-world case studies describing some of the hardest and most interesting challenges faced by one of the world's largest technology companies.

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Condense Fact from the Vapor of Nuance

  1. 1. Condense Fact from the Vapor of Nuance Mike Conover, Ph.D. Staff Data Scientist, LinkedIn
  2. 2. How do industries evolve in time? Which companies are positioned for explosive growth? Which jobs will employ the workforce in ten years? Industrial Structure YY Ahn, et al.
  3. 3. Good Problems
  4. 4. Unambiguous Concrete Response Variables Ad Clicks Real Estate Pricing Flower Species
  5. 5. Qualitative Multidimensional Non-Euclidean Expertise Nebulous TrustSerendipity Sentiment
  6. 6. High-Fidelity Proxy Variables Crowdsourcing / In-House Evaluation Tip of the Spear Operationalize “All models are wrong, but some are useful.” George Box
  7. 7. Execution
  8. 8. Quality Control
  9. 9. Scale is the Premise
  10. 10. Spectrum
  11. 11. Qualitative Evaluation Heteroscedasticity Stratified Sampling Temporal Factors
  12. 12. Confirmation Bias
  13. 13. Fit to Print
  14. 14. Lightweight Narrative Frame Toy Examples Propaganda “Given enough eyeballs, all bugs are shallow.” Eric S. Raymond
  15. 15. Infrastructure Notebooks Model Viewers Soup to Nuts
  16. 16. Ship It!
  17. 17. Devil’s Bargain Feature Transformations Transcription Errors Model Specification
  18. 18. Power Tools Singularity Something’s Wrong. Go. Spark & MLLib + Notebooks
  19. 19. Condense Fact from the Vapor of Nuance Mike Conover, Ph.D. Staff Data Scientist, LinkedIn
  20. 20. Biomorphs Iterating on Response Variables

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