Mais conteúdo relacionado Semelhante a Create an ML Factory in Financial Services with CI CD - FSI301 - New York AWS Summit (20) Mais de Amazon Web Services (20) Create an ML Factory in Financial Services with CI CD - FSI301 - New York AWS Summit1. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Felix Candelario
Creating a Machine Learning Factory
2. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Cover Slide
• Audience: Developers
• Services covered: Amazon SageMaker
• Rough level of the content: 300
• Abstract: Going from a hypothesis to a working machine learning model that infers answers in
production requires a lot of time and effort. Moreover, the ability to answer questions related to specific
results—such as, “what version of the code and data produced a particular inference?”—is paramount in highly
regulated industries such as Financial Services. Modern development practices like continuous integration and
deployment can accelerate the machine learning development process and provide a way to answer questions
about data lineage. During this talk, you will learn how to combine Amazon SageMaker (a fully managed service
that enables developers and data scientists to quickly and easily build, train, and deploy machine learning
models at any scale) with Amazon CodeCommit, CodeBuild, and CodePipeline to create a pipeline that
automatically triggers changes when either your model code or training data changes.
• Author: Felix Candelario, fcandela@
3. © 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
4. © 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
5. © 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
6. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Algorithms
Data
Programming
Models
GPUs &
Acceleration
‘Golden Age’ of Artificial Intelligence
7. © 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
8. © 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
9. © 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
10. © 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
11. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Lending decisions are highly regulated
12. © 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
13. © 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
14. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Competing requirements
15. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Continuous Integration/Continuous Delivery
16. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Solution Overview
17. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Industrialized machine learning workflow
AWS
CodeCommit AWS
CodeBuild
AWS
CodePipeline
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
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
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
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
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
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
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
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
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
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
ECR registryPipeline output
artifact bucket
Amazon
Sagemaker
Source
Train
Build
28. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Industrialized machine learning workflow
AWS
CodeCommit AWS
CodeBuild
AWS
CodePipeline
ECR registryPipeline output
artifact bucket
Amazon
Sagemaker
Source
Train
Build
29. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Deep Dive
30. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Industrialized machine learning workflow
AWS
CodeCommit AWS
CodeBuild
AWS
CodePipeline
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
ECR registryPipeline output
artifact bucket
Amazon
Sagemaker
Source
Train
Build
32. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Industrialized machine learning workflow
AWS
CodeCommit AWS
CodeBuild
AWS
CodePipeline
ECR registryPipeline output
artifact bucket
Amazon
Sagemaker
Source
Train
Build
33. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Commit Code
34. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Industrialized machine learning workflow
AWS
CodeCommit AWS
CodeBuild
AWS
CodePipeline
ECR registryPipeline output
artifact bucket
Amazon
Sagemaker
Source
Train
Build
35. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Industrialized machine learning workflow
AWS
CodeCommit AWS
CodeBuild
AWS
CodePipeline
ECR registryPipeline output
artifact bucket
Amazon
Sagemaker
Source
Train
Build
36. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Source Stage
37. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Industrialized machine learning workflow
AWS
CodeCommit AWS
CodeBuild
AWS
CodePipeline
ECR registryPipeline output
artifact bucket
Amazon
Sagemaker
Source
Train
Build
38. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Industrialized machine learning workflow
AWS
CodeCommit AWS
CodeBuild
AWS
CodePipeline
ECR registryPipeline output
artifact bucket
Amazon
Sagemaker
Source
Train
Build
39. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Build Stage
40. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Industrialized machine learning workflow
AWS
CodeCommit AWS
CodeBuild
AWS
CodePipeline
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
ECR registryPipeline output
artifact bucket
Amazon
Sagemaker
Source
Train
Build
42. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Industrialized machine learning workflow
AWS
CodeCommit AWS
CodeBuild
AWS
CodePipeline
ECR registryPipeline output
artifact bucket
Amazon
Sagemaker
Source
Train
Build
43. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Train Stage
44. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Industrialized machine learning workflow
AWS
CodeCommit AWS
CodeBuild
AWS
CodePipeline
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
ECR registryPipeline output
artifact bucket
Amazon
Sagemaker
Source
Train
Build
46. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Industrialized machine learning workflow
AWS
CodeCommit AWS
CodeBuild
AWS
CodePipeline
ECR registryPipeline output
artifact bucket
Amazon
Sagemaker
Source
Train
Build
47. © 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
48. © 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
Rekognition Transcribe Translate Polly Comprehend Lex
Amazon Mechanical Turk Amazon ML
49. © 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
50. © 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
51. Submit Session Feedback
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52. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Thank you!