Reproducible and Deployable Data Science with Open-Source Python

Reproducible and Deployable Data Science with Open-Source Python

EuroPython Conference via YouTube Direct link

Intro

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1 of 16

Intro

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Reproducible and Deployable Data Science with Open-Source Python

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  1. 1 Intro
  2. 2 The Scenario
  3. 3 Different perspectives
  4. 4 The challenges of Jupyter notebook
  5. 5 Challenges with Data Management in Jupyter Notebook
  6. 6 Declarative Data Management in Kedro
  7. 7 Parameters & Configuration Management
  8. 8 Tradeoffs
  9. 9 Challenges with managing code in Jupyter Notebook
  10. 10 Development experience in Jupyter Notebook
  11. 11 Development experience in Kedro
  12. 12 MLOPS: THE AI LIFECYCLE FOR IT PRODUCTION
  13. 13 Taking advantage of Kedro's extensibility
  14. 14 Different strategies
  15. 15 Why developing with Kedro and orchestrating with Airflow?
  16. 16 Beyond a single project

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