ML Factory Using AWS Services

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

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

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

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

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

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

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ML Factory Using AWS Services

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ML Factory Using AWS Services