In their talk, David and Michelle showed building an app using Kubeflow first with Docker Desktop and then on Docker Enterprise in the cloud. And they even took advantage of Google Cloud Tensorflow Processing Units native to the platform.

1. David Aronchick, @aronchick
Michelle Casbon, @texasmichelle
Up, Up & Away:
Cloud Native ML with
Docker Enterprise Edition

3. Make it Easy for Everyone
to Develop, Deploy and Manage
Portable, Distributed ML
on Kubernetes

4. 800+ commits
70+ community contributors
17+ companies contributing, including:
Momentum!

5. Building a model
Composability

6. Building
a model
Data ingestion Data analysis
Data
transformation
Data validation Data splitting
Trainer
Model
validation
Training
at scale
LoggingRoll-out Serving Monitoring
Composability

7. BUT.

8. Multi-Cloud is the Reality

9. And Not Just One Cloud!

10. Portability
Model
UX
Tooling
Framework
Storage
Runtime
Drivers
OS
Accelerator
HW
Experimentation

11. Portability
Building
a model
Data ingestion Data analysis
Data transfor-
mation
Data
validation
Data splitting
Trainer
Model
validation
Training
at scale
LoggingRoll-out Serving Monitoring
Model
UX
Tooling
Framework
Storage
Runtime
Drivers
OS
Accelerator
HW
Experimentation

12. Portability
Experimentation
Building
a model
Data ingestion Data analysis
Data transfor-
mation
Data
validation
Data splitting
Trainer
Model
validation
Training
at scale
LoggingRoll-out Serving Monitoring

13. Portability
Experimentation
Building
a model
Data ingestion Data analysis
Data transfor-
mation
Data
validation
Data splitting
Trainer
Model
validation
Training
at scale
LoggingRoll-out Serving Monitoring
Training

14. Portability
Experimentation
Building
a model
Data ingestion Data analysis
Data transfor-
mation
Data
validation
Data splitting
Trainer
Model
validation
Training
at scale
LoggingRoll-out Serving Monitoring
Training Cloud

15. Pfft.
That Doesn’t Matter to
Me.

16. Wrong.

20. Kubeflow &
Docker Enterprise Edition

21. Experimentation Training Cloud
Portability
Building
a model
Data ingestion Data analysis
Data transfor-
mation
Data
validation
Data splitting
Trainer
Model
validation
Training
at scale
LoggingRoll-out Serving Monitoring

22. Experimentation Training Cloud
+
Portability
Building
a model
Data ingestion Data analysis
Data transfor-
mation
Data
validation
Data splitting
Trainer
Model
validation
Training
at scale
LoggingRoll-out Serving Monitoring

23. Portability
+
Experimentation Training Cloud

24. Portability
+
Experimentation Training Cloud

25. Portability
+
Experimentation Training Cloud

26. Experimentation Training Cloud
Portability
+

27. What’s in the Box?
● Jupyter notebook
● Multi-architecture, distributed training
● Multi-framework model serving
● Examples and walkthroughs for
getting started
● Ksonnet packaging for customizing
it yourself!

28. Demo

29. Bespoke solutions
Forking of Kubernetes APIs
Reminder: What You Didn’t See
Cloud-specific, non-portable tech

30. Who’s helping? What’s next?
• Significantly easier setup
• More Kubernetes features
(e.g., autoscaling, improved
accelerators)
• More packages, frameworks and
example models
• You tell us! (Or better yet, help!)
We’re Just Getting Started!

31. Kubeflow is Open!
Open
community
Open
design
Open
source
Open
to ideas

32. GITHUB
https://github.com/kubeflow/kubeflow
SLACK
kubeflow (http://kubeflow.slack.com)
TWITTER
@kubeflow
@aronchick (aronchick@google.com)
@texasmichelle (mcasbon@google.com)