Applying DevOps Practices in Data and ML Engineering

Applying DevOps Practices in Data and ML Engineering

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[] Step 4: Processing Job

22 of 25

22 of 25

[] Step 4: Processing Job

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Applying DevOps Practices in Data and ML Engineering

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  1. 1 [] Introduction to Antoni Ivanov
  2. 2 [] Applying DevOps Practices in Data and ML Engineering
  3. 3 [] Agenda
  4. 4 [] DevOps Challenges
  5. 5 [] Versatile Data Kit
  6. 6 [] DevOps for Data as a Service
  7. 7 [] Components
  8. 8 [] VDK, SDK, and VDK Runtime Control Service
  9. 9 [] Automate and Abstract the Development Process
  10. 10 [] Quick Example: DevOps Plugin
  11. 11 [] Automate and Abstract the Data Journey
  12. 12 [] Quick Example: vdk-impala, vdk-trino
  13. 13 [] Data Journey: Ingestion
  14. 14 [] Ingestion Job
  15. 15 [] Demo
  16. 16 [] Step 1: Explore VDK's Functionalities
  17. 17 [] Step 2: Create a Data Job
  18. 18 [] Features in real-time
  19. 19 [] Working with streaming tools
  20. 20 [] VK as a training model
  21. 21 [] Step 3: Ingestion Job
  22. 22 [] Step 4: Processing Job
  23. 23 [] Step 5: Deploy
  24. 24 [] Step 6: Extend Anonymize
  25. 25 [] Step 6: Extend SQL Validation

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