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Michael Galvin, Sr. Data Scientist, Metis at MLconf ATL 2016

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Machine Learning in Business: Data science has been one of the fastest growing jobs of the past 10 years and companies are rapidly integrating it into their businesses. In this talk I will discuss the practical skills and techniques needed to successfully integrate data science into a business, as well as some common struggles and pitfalls that commonly occur.

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Michael Galvin, Sr. Data Scientist, Metis at MLconf ATL 2016

  1. 1. Practical  Machine  Learning   for  Business Michael  Galvin galvin.mj@gmail.com Twitter:  @MikeJGalvin
  2. 2. Successful  Machine  Learning  in  Business • Relationships  and  Education • Reputation  and  Buy  In • Data  Science • Preparing • Modeling  and  Implementation • Monitoring • Necessary  Skills
  3. 3. Relationships  and  Education
  4. 4. Bridging  the  Gap
  5. 5. Communication
  6. 6. Science  Experiment
  7. 7. Unicorn
  8. 8. Education
  9. 9. Business  Needs User Value
  10. 10. Current  Capabilities
  11. 11. Current  Capabilities Where  are  we?
  12. 12. Current  Capabilities Where  are  we? Where  do  we  want  to  be?
  13. 13. Reputation  and  Buy  In
  14. 14. Quick  Wins
  15. 15. Quick Wins Show  you  can  produce  something
  16. 16. Quick  Wins Show  what  you  did  was  valuable
  17. 17. Data  Science Preparing Modeling  and  Implementation Monitoring
  18. 18. Specific  Problem Data
  19. 19. Specific  Problem Data
  20. 20. Specific  Problem Data
  21. 21. What  problem  are  we  trying  to  solve?
  22. 22. Success
  23. 23. Success What  does  it  look  like?
  24. 24. Expectations
  25. 25. Expectations Feasibility
  26. 26. Expectations Feasibility Timeline
  27. 27. Expectations Feasibility Timeline Issues
  28. 28. Data  Science Preparing Modeling  and  Implementation Monitoring
  29. 29. Don’t  Over  Complicate
  30. 30. It’s  Not  Just  Algorithms Algorithms Data Assessment Result
  31. 31. Learn  About  the  Data Algorithms Data Assessment Result
  32. 32. Learn  About  the  Data Important? Algorithms Data Assessment Result
  33. 33. Learn  About  the  Data Important? Features Algorithms Data Assessment Result
  34. 34. Validation  Strategy Algorithms Data Assessment Result
  35. 35. Validation  Strategy Model  Assessment Algorithms Data Assessment Result
  36. 36. Validation  Strategy Model  Assessment More  Data  isn’t  Always  Better Algorithms Data Assessment Result
  37. 37. Get  Something  Working   Algorithms Data Assessment Result
  38. 38. Get  Something  Working   Deploying Algorithms Data Assessment Result
  39. 39. Get  Something  Working   Deploying Improve,  Iterate Algorithms Data Assessment Result
  40. 40. Are  People  Using  Your  Solution? Valuable Accessible
  41. 41. ML  Metric Business  Metric
  42. 42. Pipelines
  43. 43. Pipelines • Scalable • Reproducible • Etc.
  44. 44. Data  Science Preparing Modeling  and  Implementation Monitoring
  45. 45. Monitoring
  46. 46. Is  is  working? Feedback,  Iterate Model  Comparison
  47. 47. Work  Together,  Human  in  the  Loop
  48. 48. Necessary  Skills
  49. 49. Grit   Problem  Solving Learn  to  Learn Communicate Collaborate Passion It’s  takes  more  than  technical  skills
  50. 50. You’re  In  A  Good  Position!
  51. 51. You’re  In  A  Good  Position! Influential
  52. 52. You’re  In  A  Good  Position! Influential Impactful
  53. 53. You’re  In  A  Good  Position! Influential Impactful Drive  Change
  54. 54. Thank  You Michael  Galvin galvin.mj@gmail.com Twitter:  @MikeJGalvin

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