Video and slides synchronized, mp3 and slide download available at URL https://bit.ly/2S7lDiS. Sasha Rosenbaum shows how a CI/CD pipeline for Machine Learning can greatly improve both productivity and reliability. Filmed at qconsf.com. Sasha Rosenbaum is a Program Manager on the Azure DevOps engineering team, focused on improving the alignment of the product with open source software. She is a co-organizer of the DevOps Days Chicago and the DeliveryConf conferences, and recently published a book on Serverless computing in Azure with .NET.

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Watch the video with slide
synchronization on InfoQ.com!
https://www.infoq.com/presentations/
ci-cd-ml/

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Highlights
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Presented at QCon San Francisco
www.qconsf.com

4. MLOps
CI/CD for Machine Learning
SASHA ROSENBAUM

5. Sasha Rosenbaum
Sr. Program Manager
@DivineOps

6. https://www.deliveryconf.com/

7. Agenda
§Machine Learning 101
§ML CI/CD Pipeline Overview
§Potential implementation
§Demo

8. Trigger the pipeline!

9. What is MLOps
and
WHY should you care?

10. Machine Learning (ML)
Is the science of getting computers to act
Without being explicitly programmed

12. Machine Learning vs DevOps Google searches

13. Python
questions on
Stack
Overflow

14. OK, but why should YOU care?

15. 13

16. Data Scientists just want to Data Science

17. Deep Learning
Some ML training
algorithms are
complex

18. Deep Learning - Backpropagation
Some ML training
algorithms are
complex

19. Typical data
scientist work
environment

20. We’ve got
the notebook
into source
control!

21. Programming
Algorithm
Data
Answers

22. Machine Learning
Algorithm
Data
Answers

23. Model
Data
Answers
Machine Learning

24. Model
Data
Answers
Machine Learning

25. Predictions
Data
Model
Data
Answers
Machine Learning

26. How do we
put the
model in
production?

27. What is an ML model?

28. Linear Regression – Housing Prices
The training
finds a and b
such that
Y = a+bX+ϵ

29. Deep Learning
The input and
output may be
vectors !𝑋, !𝑌

30. Image Classification

31. ML Model
A definition of the mathematical formula
with a number of parameters that
are learned from the data

32. Isn’t this
just an API
endpoint?!

33. Do models really change that often?

34. Models must be improved continuously

35. The dataset matters!

44. The model predictions depend
on what it has “seen”

45. => Dataset is part of the model
version!

46. FBLearner FlowTensorFlow Extended
Uber’s Michelangelo Microsoft Aether

47. But I don’t work at a
big company with
thousands of
ML engineers!

48. How do we iterate?

49. Machine Learning Lifecycle

50. DevOps/SREData Scientist
• Quick iteration
• Versioning
• Reuse
• Great tools
• Ease of
management
• Unlimited scale
• Eliminating drift
• Quick iteration
• Versioning
• Reuse
• Compliance
• Observability
• Uptime
• UpdatesFriends?

51. App developer
MLOps Workflow
Build appCollaborate Test app Release app Monitor app
Model reproducibility Model retrainingModel deploymentModel validation
Data scientist

52. Code, dataset, and
environment versioning
Model reproducibility Model retrainingModel deploymentModel validation
Build appCollaborate Test app Release app Monitor app
MLOps Workflow
App developer
Data scientist

53. MLOps Workflow
Model reproducibility Model retrainingModel deploymentModel validation
Automated ML
ML Pipelines
Hyperparameter tuning
Train model
Build appCollaborate Test app Release app Monitor app
App developer
Data scientist

54. MLOps Workflow
Model validation
& certification
Model reproducibility Model retrainingModel deploymentModel validation
Train model Validate model
Build appCollaborate Test app Release app Monitor app
App developer
Data scientist

55. MLOps Workflow
Model packaging
Simple deployment
Model reproducibility Model retrainingModel deploymentModel validation
Train model Validate model Deploy
model
Build appCollaborate Test app Release app Monitor app
App developer
Data scientist

56. MLOps Workflow
Model
management
& monitoring
Model performance
analysis
Model reproducibility Model retrainingModel deploymentModel validation
Train model Validate model Deploy
model
Monitor
model
Retrain model
Build appCollaborate Test app Release app Monitor app
App developer
Data scientist

57. Build Your Own MLOps Platform
+ +

58. ML Pipeline
A reusable, scaleable ML
workflow template
Kubeflow pipeline
A reusable, scalable ML
workflow template that runs on
containers

59. Azure ML
• Prep data
• Train
• Test
• Deploy
• Manage

60. Demo

61. Even a simple CI/CD pipeline is
better than none!

62. DevOps
Because
change is the
only constant
in life

63. AI Ethics

64. Bias is a property of
information

65. We must build AI responsibly

66. Build AI responsibly!

67. Thank You!
@DivineOps

68. Questions?

69. Resources
GitHub repo
https://www.kubeflow.org/docs/azure/azureendtoend/
Deploy Kubeflow on Azure
https://www.kubeflow.org/docs/azure/deploy/install-kubeflow/
Example Kubeflow Azure Pipeline
https://www.kubeflow.org/docs/azure/azureendtoend/
Release pipeline
https://dev.azure.com/sasrose/kubeflow/_release

70. Watch the video with slide
synchronization on InfoQ.com!
https://www.infoq.com/presentations/
ci-cd-ml/