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Amanda Casari, Senior Data Scientist, Concur at MLconf SEA - 5/20/16


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Scaling Data Science Products, Not Data Science Teams: Congratulations! Your data science feature works! Your metrics are outstanding. Your data scientists and engineers have created useful products for your customers with proven results. Now your product and marketing teams are ready to move into new markets. Your underlying population changes. The skewed statistics of your data shifts depending on the data center you analyze. Your product is now localized and your NLP methods must adjust for a greater range of languages. Your requirements have grown. Your team has not.

How do you scale success for global products in multiple data centers with small teams?

The Data Science team at Concur’s work to grow our products into international markets has not required a global scaling of resources. This talk will share our lessons learned in creating modular, reusable data science products deployable to international, segregated data centers.

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Amanda Casari, Senior Data Scientist, Concur at MLconf SEA - 5/20/16

  1. 1. SCALING DATA SCIENCE PRODUCTS NOT DATA SCIENCE TEAMS long winded views of scaling up @amcasari MLconf seattle, 2016may20 nasa @
  2. 2. data science via random walks senior product manager + senior data scientist @ Concur Labs control systems engineering + robotics + legos officer in USN operations research analyst wandering dirtbag + conservation volunteer EE + applied math + complex systems underwater robotics consultant extraordinaire SAHM
  3. 3. @ ?
  4. 4. • Is this a Product Design problem? • Is this a Mathy ML problem? • Is this a Software Engineering problem? @ data science therapy: let’s talk about your problems
  5. 5. how do we keep new customers happy? @
  6. 6. product design problem: managing user expectation @
  7. 7. how are we going to maintain so many more models? @
  8. 8. software engineering problem: scale a code base @
  9. 9. how can we use the same code when we have different customer bases? @
  10. 10. mathy ML problem: feature design @
  11. 11. how accurate will your product be in the new market? @
  12. 12. software engineering problem: test test test @
  13. 13. how are you going to be personalized for a customer base you don’t know? @
  14. 14. mathy ML problem: cold start @
  15. 15. how do we know you are right? @
  16. 16. product design problem: identifying feedback loops @
  17. 17. {THANKS MUCH} @amcasari