Condense Fact from the Vapor of Nuance
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. Condense Fact from the Vapor of Nuance Mike Conover, Ph.D. Staff Data Scientist, LinkedIn
- 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.
- 4. Unambiguous Concrete Response Variables Ad Clicks Real Estate Pricing Flower Species
- 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. Execution
- 9. Scale is the Premise
- 10. Spectrum
- 13. Fit to Print
- 14. Lightweight Narrative Frame Toy Examples Propaganda “Given enough eyeballs, all bugs are shallow.” Eric S. Raymond
- 16. Ship It!
- 17. Devil’s Bargain Feature Transformations Transcription Errors Model Specification
- 18. Power Tools Singularity Something’s Wrong. Go. Spark & MLLib + Notebooks
- 19. Condense Fact from the Vapor of Nuance Mike Conover, Ph.D. Staff Data Scientist, LinkedIn
- 20. Biomorphs Iterating on Response Variables