Creating an ML Factory with CI/CD Pipelines

1. Creating a Machine Learning Factory F e l i x C a n d e l a r i o

2. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Creating a Machine Learning Factory R e g u l a t o r y o b l i g a t i o n s r e q u i r e w o r k l o a d s t h a t r e l y o n M L b e o p e r a t i o n a l i z e d A S A P A p p l y i n g m o d e r n C I / C D p r a c t i c e s t o M L w o r k l o a d s i s t h e f a s t e s t w a y f o r w a r d A W S i s t h e b e s t p l a c e t o o p e r a t i o n a l i z e y o u r M L w o r k l o a d s W h y H o w W h e r e O p e r a t i o n a l i z i n g M L w i t h C I / C D p i p e l i n e s

3. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 01 05 10 15 20 25 Introduction ML in Banking: Credit Scoring Regulatory Rmplications Operationalizing ML on AWS Why ML on AWS? Conclusion Contents

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. — J e f f B e z o s

6. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 01 05 10 15 20 25 Introduction ML in Banking: Credit Scoring Regulatory Implications Operationalizing ML on AWS Why ML on AWS? Conclusion Contents

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

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

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

16. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Industrialized Machine Learning Workflow AWS CodeCommit AWS CodeBuild AWS CodePipeline ECR registry Pipeline output artifact bucket Amazon Sagemaker Source Train Build

17. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. AWS CodeCommit AWS CodeBuild AWS CodePipeline ECR registry Pipeline output artifact bucket Amazon Sagemaker Source Train Build Industrialized Machine Learning Workflow

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 Rekognition Transcribe Translate Polly Comprehend Lex Amazon Mechanical Turk Amazon ML

49. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Creating a Machine Learning Factory R e g u l a t o r y o b l i g a t i o n s r e q u i r e w o r k l o a d s t h a t r e l y o n M L b e o p e r a t i o n a l i z e d A S A P A p p l y i n g m o d e r n C I / C D p r a c t i c e s t o M L w o r k l o a d s i s t h e f a s t e s t w a y f o r w a r d A W S i s t h e b e s t p l a c e t o o p e r a t i o n a l i z e y o u r M L w o r k l o a d s W h y H o w W h e r e O p e r a t i o n a l i z i n g M L w i t h C I / C D p i p e l i n e s

50. Thank you f c a n d e l a @ a m a z o n . c o m

Creating an ML Factory with CI/CD Pipelines

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Creating an ML Factory with CI/CD Pipelines