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Create an ML Factory in Financial Services with CI/CD - FSI301 - Toronto AWS Summit

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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.

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Create an ML Factory in Financial Services with CI/CD - FSI301 - Toronto AWS Summit

  1. 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. 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. 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. 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. 5. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Algorithms Data Programming Models GPUs & Acceleration Golden Age of Artificial Intelligence
  6. 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. 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. 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. 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. 10. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Lending decisions are highly regulated
  11. 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. 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. 13. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Competing requirements
  14. 14. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Continuous Integration/Continuous Delivery
  15. 15. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Solution Overview
  16. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 28. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Deep Dive
  29. 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. 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. 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. 32. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Commit Code
  33. 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. 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. 35. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Source Stage
  36. 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. 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. 38. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Build Stage
  39. 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. 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. 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. 42. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Train Stage
  43. 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. 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. 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. 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. 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. 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. 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. 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. 51. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Thank you!

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