The Data Janitor Returns

The Data Janitor Returns

MLCon | Machine Learning Conference via YouTube Direct link

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30 of 45

30 of 45

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Classroom Contents

The Data Janitor Returns

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  1. 1 Introduction
  2. 2 Challenges in the industry
  3. 3 Feedback
  4. 4 Hard curve over
  5. 5 Building the pyramid
  6. 6 Making programmatic KPI definitions
  7. 7 Google Analytics
  8. 8 Tableau
  9. 9 Project Adventures
  10. 10 The Pyramid
  11. 11 AV Testing
  12. 12 Funding
  13. 13 Leftovers
  14. 14 Machine Learning
  15. 15 Reality
  16. 16 One constructive approach
  17. 17 Data DevOps
  18. 18 Compression
  19. 19 Imagenet
  20. 20 A Cigar
  21. 21 Park
  22. 22 Data Engineering
  23. 23 Open Roots
  24. 24 Our original sin
  25. 25 The problem with distributed computing
  26. 26 Any advice
  27. 27 What makes a data engineer
  28. 28 Cloud agnostic advice
  29. 29 Better quality code
  30. 30 Transit
  31. 31 Hadoop
  32. 32 Showdown
  33. 33 Spark
  34. 34 Presto
  35. 35 Redshift vs Presto
  36. 36 Clickhouse
  37. 37 Google
  38. 38 Open Source
  39. 39 The Shepherd
  40. 40 The Bird of Prey
  41. 41 Making a decision
  42. 42 Finding data engineers
  43. 43 Marketing and data integration
  44. 44 Y2K
  45. 45 Outro

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