Financial institutions want to accelerate and scale their use of machine learning (ML), but going from a hypothesis to a working ML model that infers answers in production requires much time and effort. Continuous integration and deployment techniques can help by accelerating the ML development process while providing a way to answer questions about data lineage, such as, "What version of the code and data produced this particular inference?" In this session, learn how to combine Amazon SageMaker with AWS CodeCommit, AWS CodeBuild, and AWS CodePipeline to create a workflow that helps provide the reproducibility and auditability that financial institutions need without constraining the tools and methods that data scientists use to build their ML models.

1. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Felix Candelario
FSI301
Create an ML Factory in Financial Services
with CI/CD

2. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Creating a machine learning factory
Regulatory obligations require
workloads that rely on ML be
operationalized ASAP
Why
Applying modern CI/CD
practices to ML workloads is
the fastest way forward
How
AWS is the best place to
operationalize your ML
workloads
Where

3. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Contents
Introduction
ML in Banking: Credit scoring
Regulatory implications
Operationalizing ML on AWS
Why ML on AWS?
Conclusion

4. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
“It is a renaissance. It is a golden age. We are now
solving problems with machine learning and artificial
intelligence that were … in the realm of science fiction
for the last several decades.”
— Jeff Bezos

5. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Algorithms
Data
Programming
Models
GPUs &
Acceleration
Golden Age of Artificial Intelligence

6. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Contents
Introduction
ML in Banking: Credit scoring
Regulatory implications
Operationalizing ML on AWS
Why ML on AWS?
Conclusion

7. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
ML in Banking: Marketplace lenders
• Operating exclusively online
• Niche product focus
• High degree of automation
• User of non-traditional data
sources
• Rapid changes in decision
criteria and scoring models
Typical Characteristics
• Unsecured personal loans
• Education lending
• SMB loans and credit lines
• Real estate secured
Example products & lenders

8. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
ML in Banking: Non-traditional data sources
• Payday and non-prime loan
information
• Check cashing services
• Rent-to-own transactions
• Mobile phone account openings
and payments
• Utility accounts & payments
Non-traditional data
• Social media and web surfing data
• Address stability
• Number and age of email
addresses
• Local unemployment rates
• Profession or job function

9. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Contents
Introduction
ML in Banking: Credit scoring
Regulatory implications
Operationalizing ML on AWS
Why ML on AWS?
Conclusion

10. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Lending decisions are highly regulated

11. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
ML for FSI workloads requires Industrialization
• Development happens on dev
desktops
• Iterative process that is prone to
experimentation
• Tooling, frameworks, and languages
in constant flux
• Difficult to acquire infrastructure
ML today is very artisanal
• Credit lifecycle processes moving
from decision trees to ML
• Highly regulated credit lifecycles
• Fair Lending, Fair Housing, GDPR
• Disparate impact is terrifying
FSI workloads require rigor

12. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Contents
Introduction
ML in Banking: Credit scoring
Regulatory implications
Operationalizing ML on AWS
Why ML on AWS?
Conclusion

13. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Competing requirements

14. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Continuous Integration/Continuous Delivery

15. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Solution Overview

16. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Industrialized machine learning workflow
AWS
CodeCommit AWS
CodeBuild
AWS
CodePipeline
Amazon
ECR registryPipeline output
artifact bucket
Amazon
SageMaker
Source
Train
Build

17. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Industrialized machine learning workflow
AWS
CodeCommit AWS
CodeBuild
AWS
CodePipeline
Amazon
ECR registryPipeline output
artifact bucket
Amazon
SageMaker
Source
Train
Build

18. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Industrialized machine learning workflow
AWS
CodeCommit AWS
CodeBuild
AWS
CodePipeline
Amazon
ECR registryPipeline output
artifact bucket
Amazon
SageMaker
Source
Train
Build

19. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Industrialized machine learning workflow
AWS
CodeCommit AWS
CodeBuild
AWS
CodePipeline
Amazon
ECR registryPipeline output
artifact bucket
Amazon
SageMaker
Source
Train
Build

20. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Industrialized machine learning workflow
AWS
CodeCommit AWS
CodeBuild
AWS
CodePipeline
Amazon
ECR registryPipeline output
artifact bucket
Amazon
SageMaker
Source
Train
Build

21. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Industrialized machine learning workflow
AWS
CodeCommit AWS
CodeBuild
AWS
CodePipeline
Amazon
ECR registryPipeline output
artifact bucket
Amazon
SageMaker
Source
Train
Build

22. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Industrialized machine learning workflow
AWS
CodeCommit AWS
CodeBuild
AWS
CodePipeline
Amazon
ECR registryPipeline output
artifact bucket
Amazon
SageMaker
Source
Train
Build

23. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Industrialized machine learning workflow
AWS
CodeCommit AWS
CodeBuild
AWS
CodePipeline
Amazon
ECR registryPipeline output
artifact bucket
Amazon
SageMaker
Source
Train
Build

24. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Industrialized machine learning workflow
AWS
CodeCommit AWS
CodeBuild
AWS
CodePipeline
Amazon
ECR registryPipeline output
artifact bucket
Amazon
SageMaker
Source
Train
Build

25. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Industrialized machine learning workflow
AWS
CodeCommit AWS
CodeBuild
AWS
CodePipeline
Amazon
ECR registryPipeline output
artifact bucket
Amazon
SageMaker
Source
Train
Build

26. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Industrialized machine learning workflow
AWS
CodeCommit AWS
CodeBuild
AWS
CodePipeline
Amazon
ECR registryPipeline output
artifact bucket
Amazon
SageMaker
Source
Train
Build

27. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Industrialized machine learning workflow
AWS
CodeCommit AWS
CodeBuild
AWS
CodePipeline
Amazon
ECR registryPipeline output
artifact bucket
Amazon
SageMaker
Source
Train
Build

28. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Deep Dive

29. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Industrialized machine learning workflow
AWS
CodeCommit AWS
CodeBuild
AWS
CodePipeline
Amazon
ECR registryPipeline output
artifact bucket
Amazon
SageMaker
Source
Train
Build

30. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Industrialized machine learning workflow
AWS
CodeCommit AWS
CodeBuild
AWS
CodePipeline
Amazon
ECR registryPipeline output
artifact bucket
Amazon
SageMaker
Source
Train
Build

31. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Industrialized machine learning workflow
AWS
CodeCommit AWS
CodeBuild
AWS
CodePipeline
Amazon
ECR registryPipeline output
artifact bucket
Amazon
SageMaker
Source
Train
Build

32. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Commit Code

33. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Industrialized machine learning workflow
AWS
CodeCommit AWS
CodeBuild
AWS
CodePipeline
Amazon
ECR registryPipeline output
artifact bucket
Amazon
SageMaker
Source
Train
Build

34. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Industrialized machine learning workflow
AWS
CodeCommit AWS
CodeBuild
AWS
CodePipeline
Amazon
ECR registryPipeline output
artifact bucket
Amazon
SageMaker
Source
Train
Build

35. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Source Stage

36. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Industrialized machine learning workflow
AWS
CodeCommit AWS
CodeBuild
AWS
CodePipeline
Amazon
ECR registryPipeline output
artifact bucket
Amazon
SageMaker
Source
Train
Build

37. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Industrialized machine learning workflow
AWS
CodeCommit AWS
CodeBuild
AWS
CodePipeline
Amazon
ECR registryPipeline output
artifact bucket
Amazon
SageMaker
Source
Train
Build

38. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Build Stage

39. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Industrialized machine learning workflow
AWS
CodeCommit AWS
CodeBuild
AWS
CodePipeline
Amazon
ECR registryPipeline output
artifact bucket
Amazon
SageMaker
Source
Train
Build

40. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Industrialized machine learning workflow
AWS
CodeCommit AWS
CodeBuild
AWS
CodePipeline
Amazon
ECR registryPipeline output
artifact bucket
Amazon
SageMaker
Source
Train
Build

41. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Industrialized machine learning workflow
AWS
CodeCommit AWS
CodeBuild
AWS
CodePipeline
Amazon
ECR registryPipeline output
artifact bucket
Amazon
SageMaker
Source
Train
Build

42. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Train Stage

43. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Industrialized machine learning workflow
AWS
CodeCommit AWS
CodeBuild
AWS
CodePipeline
Amazon
ECR registryPipeline output
artifact bucket
Amazon
SageMaker
Source
Train
Build

44. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Industrialized machine learning workflow
AWS
CodeCommit AWS
CodeBuild
AWS
CodePipeline
Amazon
ECR registryPipeline output
artifact bucket
Amazon
SageMaker
Source
Train
Build

45. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Industrialized machine learning workflow
AWS
CodeCommit AWS
CodeBuild
AWS
CodePipeline
Amazon
ECR registryPipeline output
artifact bucket
Amazon
SageMaker
Source
Train
Build

46. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Contents
Introduction
ML in Banking: Credit scoring
Regulatory implications
Operationalizing ML on AWS
Why ML on AWS?
Conclusion

47. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Why ML on AWS?
PLATFORM SERVICES
APPLICATION SERVICES
FRAMEWORKS & INTERFACES
Caffe2 CNTK
Apache
MXNet
PyTorch TensorFlow Torch Keras Gluon
AWS Deep Learning AMIs
Amazon SageMaker AWS DeepLens
Amazon
Rekognition
Amazon
Transcribe
Amazon
Translate
Amazon Polly
Amazon
Comprehend
Amazon Lex
Amazon Mechanical Turk Amazon ML

48. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Contents
Introduction
ML in Banking: Credit scoring
Regulatory implications
Operationalizing ML on AWS
Why ML on AWS?
Conclusion

49. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Creating a machine learning factory
Regulatory obligations
require workloads that rely
on ML be operationalized
ASAP
Why
Applying modern CI/CD
practices to ML workloads
is the fastest way forward
How
AWS is the best place to
operationalize your ML
workloads
Where

50. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Submit session feedback
1. Tap the Schedule icon.
2. Select the session you attended.
3. Tap Session Evaluation to submit your
feedback.

51. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Thank you!