Data science teams have different levels of maturity and they need to be equipped with the right tools and infrastructure to make them more agile and ready. Here, I will be discussing a combination of open source tools and cloud managed services that can go hand-by-hand and grow with your data science teams needs as they mature.

1. Buying a Ferrari for your
teenager?
You may want to think twice

2. Skills
Road
Vehicle

4. Skills
Infrastructure
Algorithmic
complexity

5. Luis Vaquero
- PhD in Cardiology, MSc Pharmacology
- PhD in Distrib Systems, MSc EE, BSc Electronics
- Reader in Data Systems at Uni. of Bristol, UK
- Data at Telefonica/O2, Hewlett-Packard Labs, HPE,
Dyson
- Director of Data at GoCo Group

6. In the beginning
● Small data
● Simple algorithms (regression)
● No tests, no CI, no
infrastructure, no monitoring

7. Downsampling
Model split
Data split

8. Skills
Infrastructure
Algorithmic
complexity

9. First Steps
● Bigger data
● Slightly more complex
algorithms (e.g. xgboost)
● No tests, no CI, no
infrastructure, no monitoring

10. Skills
Infrastructure
Algorithmic
complexity

11. ?

13. Skills
Infrastructure
Algorithmic
complexity

15. https://dl.acm.org/citation.cfm
?id=3098021
https://engineering.fb.com/core-
data/introducing-fblearner-flow-
facebook-s-ai-backbone/

17. https://medium.com/netflix-techblog/open-sourcing-metaflow-a-human-centric-framework-for-data-science-fa72e04a5d9

18. Skills
Infrastructure
Algorithmic
complexity

20. https://cloud.google.com/blog/products/ai-machine-learning/best-practices-that-can-improve-the-life-of-any-developer-using-jupyter-notebooks

21. The Unicorn Data Person

23. Team b4 Infrastructure: The Notebooks Manifesto
1. Follow established software development best practices
2. Version control and Continuous Integration/Deployment (CI/CD)
3. Parameterised Notebooks
4. Log All Experiments Automatically -- Monitor
5. Sharing is Caring

24. Software Best Practices: Single Responsibility
#export annotation in our notebook cells to export them into a
single module that can be reused later

25. Software Best Practices: Test
#test annotation in our notebook cells to run them and assess
their output

26. Skills
Infrastructure
Algorithmic
complexity

27. Team b4 Infrastructure: The Notebooks Manifesto
1. Follow established software development best practices
2. Version control and Continuous Integration/Deployment (CI/CD)
3. Parameterised Notebooks
4. Log All Experiments Automatically -- Monitor
5. Sharing is Caring

28. Version Control and CI
Dev Kubernetes Cluster
Kubeflow GCS
Save
Trained
model

29. Version Control and CI
Dev Kubernetes Cluster
Kubeflow
merge
review
GCS
Save
Trained
model
push pull
Git repository

31. Version Control and CI
Dev Kubernetes Cluster
Kubeflow
merge
review
GCS
Save
Trained
model
push pull
Git repository
Build System
trigger

32. Version Control and CI
Dev Kubernetes Cluster
Kubeflow
merge
review
GCS
Save
Trained
model
push pull
Git repository
Build System
trigger
Container Registry
publish

33. Version Control and CI
https://medium.com/kubeflow/automating-jupyter-notebook-deployments-to-kubeflow-pipelines-with-kale-a4ede38bea1f

34. Version Control and CI
https://medium.com/kubeflow/automating-jupyter-notebook-deployments-to-kubeflow-pipelines-with-kale-a4ede38bea1f

35. Version Control and CI
Dev Kubernetes Cluster
Kubeflow
merge
review
GCS
Save
Trained
model
push pull
Git repository
Build System
trigger
Container Registry
publish
Local Notebook
Load
Trained
model

36. Version Control and CI
Dev Kubernetes Cluster
Kubeflow
merge
review
GCS
Save
Trained
model
push pull
Git repository
Build System
trigger
Container Registry
publish
Local Notebook
Load
Trained
model
push

37. Version Control and CI
Dev Kubernetes Cluster
Kubeflow
merge
review
GCS
Save
Trained
model
push pull
Git repository
Build System
trigger
Container Registry
publish
Local Notebook
Load
Trained
model
push
Prod Kubernetes Cluster
deploy

38. Skills
Infrastructure
Algorithmic
complexity

39. Team b4 Infrastructure: The Notebooks Manifesto
1. Follow established software development best practices
2. Version control and Continuous Integration/Deployment (CI/CD)
3. Parameterised Notebooks
4. Log All Experiments Automatically -- Monitor
5. Sharing is Caring

40. Parametrised Notebooks
Tweak and Run
● Analysts who can
tweak code
● Use dedicated
jupyterhub server
● Notebook templates
Visual results
● Analysis who need to
see results
● Use Voila to run
automatically
Auto: Test and Exec
● Tag cells as #test
and let Papermill to
evaluate results of
the test output cells
● Run Notebooks “as a
remote script”

41. Skills
Infrastructure
Algorithmic
complexity

42. Team b4 Infrastructure: The Notebooks Manifesto
1. Follow established software development best practices
2. Version control and Continuous Integration/Deployment (CI/CD)
3. Parameterised Notebooks
4. Log All Experiments Automatically -- Monitor
5. Sharing is Caring

43. Monitor Support Playbooks
Infrastructure
Monitoring
Model
Monitoring
Input Data
Monitoring
Experiment
Monitoring

44. Skills
Infrastructure
Algorithmic
complexity

45. Team b4 Infrastructure: The Notebooks Manifesto
1. Follow established software development best practices
2. Version control and Continuous Integration/Deployment (CI/CD)
3. Parameterised Notebooks
4. Log All Experiments Automatically -- Monitor
5. Sharing is Caring

46. Sharing is Caring: search
https://github.com/nbgallery/nbgallery

47. Sharing is Caring
https://github.com/nbgallery/nbgallery

48. Skills
Infrastructure
Algorithmic
complexity

49. Thank you!
luis.vaquero@{gocompare.com/bristol.ac.uk}
I hate vehicles..
https://www.linkedin.com/pulse/notebooks-execution-unit-i-wrong-luis-m-vaquero/
Josh Bottum and the
#kubeflow
community for
keeping us in the loop