Practical DevOps for the busy data scientist

Transcript

1. Tania Allard, PhD @ixek Developer Advocate @Microsoft Practical DevOps for

   the busy Data Scientist http://bit.ly/MancML-trallard

2. 2 A bit of background never hurt anyone About us

3. 3 @ixek

4. 4 @ixek

5. 5 Top top view… @ixek Stable model/application ready to be

   productised R&D - develop, iterate fast, usually local or cloud Magic Is it live??

6. 6 How I would like everything to work…. @ixek It

   works…. now send it over to production R&D - develop, iterate fast, usually local or cloud Push code, tag, tag data* Worry free deployment! Wait and relax

7. 7 @ixek

8. 8

9. 9 @ixek DevOps / DataOps / MLOps

10. 10 DevOps is the union of people, process, and products

    to enable continuous delivery of value into production What is DevOps anyway? @ixek

11. 11 Sort of DevOps applied to data-intensive applications. Requires close

    collaboration between engineers, data scientists, architects, data engineers and Ops. How does it fit for DS? @ixek

12. 12 @ixek Aims to reduce the end-to-end cycle time of

    data analytics/science from the origin of ideas to the creation of data artifacts.

13. 13

14. 14

15. 15 7 steps to DS

16. 16 Keep everything in source control - but allow for

    experimentation

17. 17

18. 18 Standardize and define your environments in code (conda, pipfiles,

    Docker)

19. 19 Use canonical data sources - always know what data

    you are using (where it comes and goes)

20. 20

21. 21 Automate wisely

22. 22 https://xkcd.com/1205/

23. 23

24. 24 Use pipelines for repeatability and explainability

25. 25 Deploy portable models

26. 26

27. 27 Test continuously and monitor production: shift left

28. 28

29. 29 Thank you @ixek http://bit.ly/MancML-trallard