Lessons Learned from Big Data and AI/ML Collaboration for Giant Hogweed Eradication

Lessons Learned from Big Data and AI/ML Collaboration for Giant Hogweed Eradication

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Intro

1 of 29

1 of 29

Intro

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Lessons Learned from Big Data and AI/ML Collaboration for Giant Hogweed Eradication

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  1. 1 Intro
  2. 2 Self introduction
  3. 3 Outline
  4. 4 Our journey to apply deep learning for giant hogweed eradication
  5. 5 About giant hogweed
  6. 6 Project Overview
  7. 7 Data volumes
  8. 8 Various specialty
  9. 9 Challenges of the project
  10. 10 Architecture overview
  11. 11 Data flow-1/4: Data preparation phase
  12. 12 Assistance tool for data preparation
  13. 13 Inference processing by Apache Spark
  14. 14 Data flow-4/4: Data analysis phase
  15. 15 Lessons Learned from a consideration of architecture design
  16. 16 Common view in Machine learning system
  17. 17 Scaled-ML systems
  18. 18 ML Application with scalability on our architecture
  19. 19 Model development vs. Model operating
  20. 20 Dev-friendly to Dev-friendly
  21. 21 Dev-friendly to Ops-friendly
  22. 22 Use case: Uber implemented ML Ops on Spark
  23. 23 Ops-friendly to Ops-friendly Patterns that selected a toolset familiar to Model Operator for both model development and operation
  24. 24 Use case: Twitter leveraged Scala for feature engineering
  25. 25 Example software: BigDL by Intel
  26. 26 Patterns of software choices Reprint Four patterns of the workflow regarding combination of Model Development phase and Model Operating phase
  27. 27 Architecture and data pipeline ver.2.0
  28. 28 Tips: Abstraction of functions used in applications
  29. 29 Tips: Detecting and storing deterioration of confidence

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