Lessons Learned From Machine Learning Pipelines in Production

Lessons Learned From Machine Learning Pipelines in Production

Linux Foundation via YouTube Direct link

Intro

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1 of 16

Intro

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Lessons Learned From Machine Learning Pipelines in Production

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  1. 1 Intro
  2. 2 Outline
  3. 3 1. Background: Machine learning in production
  4. 4 2. Assumption: Job specialization in machine learning projects
  5. 5 1-3. Issue for applying logics into production environment
  6. 6 1-4. Gaps between experimental and production environment
  7. 7 1-5. Challenges towards production environment
  8. 8 1-7. Overview of validation scenario and its target ML system
  9. 9 1. Utilizing Kedro to overcome challenges
  10. 10 3-1. Solution 1: Transforming pipelines in Kedro style
  11. 11 2-3-2. Step 1-A: Project Template Generation by Kedro
  12. 12 2-3-4. Step 1-C: Adding node not in notebook
  13. 13 2-3-6. Step 1-D: Connecting nodes to develop pipeline
  14. 14 2-5-2. Solution 3. Removing loop inside nodes extracted from Jupyter notebook
  15. 15 1. What we learned in validation scenario: good points
  16. 16 3. Possible solution

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