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Reactive for Machine Learning Teams

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Presented at O'Reilly's Software Architecture Conference NYC 2017

Publicada em: Tecnologia
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Reactive for Machine Learning Teams

  1. 1. Reactive for Machine Learning Teams Jeff Smith @jeffksmithjr
  2. 2. Context
  3. 3. Machine Learning
  4. 4. Classification
  5. 5. History
  6. 6. Naive ML Architecture
  7. 7. Components
  8. 8. Data Collectors
  9. 9. Pipelines Raw Data FeaturesFeature Generation Pipeline Raw Data FeaturesFeature Generation Pipeline
  10. 10. Model Publishers
  11. 11. Model Servers and Microservices
  12. 12. Model Metrics
  13. 13. Model Supervisors
  14. 14. Reactive
  15. 15. Reactive
  16. 16. Reactive Traits
  17. 17. Responsive
  18. 18. Elastic
  19. 19. Resilient
  20. 20. Message Driven
  21. 21. Reactive Strategies
  22. 22. Reactive Machine Learning
  23. 23. Data
  24. 24. Data
  25. 25. Data
  26. 26. Getting Reactive
  27. 27. Big, Fast, & Hairy
  28. 28. Beginning
  29. 29. Time Communication Failure Load Request Response System
  30. 30. Reactive Traits
  31. 31. Reactive Strategies
  32. 32. Building on Success
  33. 33. Metrics & Instrumentation
  34. 34. Containment
  35. 35. Impact
  36. 36. For Later
  37. 37. Use the code ctwsacon17 for 40% off
  38. 38. x.ai @xdotai hello@human.x.ai New York, New York
  39. 39. Questions @jeffksmithjr

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