Machine Learning Lens AWS Well-Architected Framework Machine Learning Lens AWS Well-Architected Framework Machine Learning Lens: AWS Well-Architected Framework Copyright © Amazon Web Services, Inc. and/or its affiliates. All rights reserved. Amazon's trademarks and trade dress may not be used in connection with any product or service that is not Amazon's, in any manner that is likely to cause confusion among customers, or in any manner that disparages or discredits Amazon. All other trademarks not owned by Amazon are the property of their respective owners, who may or may not be affiliated with, connected to, or sponsored by Amazon. Machine Learning Lens AWS Well-Architected Framework Table of Contents ........................................................................................................................................................ v Abstract and Introduction ................................................................................................................... 1 Abstract .................................................................................................................................... 1 Introduction .............................................................................................................................. 1 Definitions ......................................................................................................................................... 2 Machine Learning Stack .............................................................................................................. 2 AI Services ........................................................................................................................ 2 ML Services ....................................................................................................................... 2 ML Frameworks and Infrastructure ....................................................................................... 3 Combining Levels ............................................................................................................... 3 Phases of ML Workloads ............................................................................................................. 3 Business Goal Identification ................................................................................................. 4 ML Problem Framing .......................................................................................................... 4 Data Collection .................................................................................................................. 5 Data Preparation ................................................................................................................ 5 Data Visualization and Analytics .......................................................................................... 6 Feature Engineering ........................................................................................................... 7 Model Training ................................................................................................................... 8 Model Evaluation and Business Evaluation ............................................................................ 9 General Design Principles .................................................................................................................. 11 Scenarios ......................................................................................................................................... 12 Build Intelligent Applications using AWS AI Services ..................................................................... 12 Reference Architecture ...................................................................................................... 13 Adding Sophistication ....................................................................................................... 14 Using AI services with your Data ........................................................................................ 15 Use Managed ML Services to Build Custom ML Models .................................................................. 15 Reference Architecture ...................................................................................................... 16 Managed ETL Services for Data Processing .................................................................................. 17 Reference Architecture ...................................................................................................... 17 Machine Learning on Edge and on Multiple Platforms ................................................................... 18 Reference Architecture ...................................................................................................... 19 Model Deployment Approaches .................................................................................................. 20 Standard Deployment ....................................................................................................... 21 Blue/Green Deployments ................................................................................................... 21 Canary Deployment .......................................................................................................... 23 A/B Testing ..................................................................................................................... 23 The Pillars of the Well-Architected Framework ..................................................................................... 25 Operational Excellence Pillar ...................................................................................................... 25 Design Principles .............................................................................................................. 25 Best Practices .................................................................................................................. 26 Resources ........................................................................................................................ 32 Security Pillar .......................................................................................................................... 32 Design Principles .............................................................................................................. 32 Best Practices .................................................................................................................. 33 Resources ........................................................................................................................ 38 Reliability Pillar ........................................................................................................................ 38 Design Principles .............................................................................................................. 38 Best Practices .................................................................................................................. 38 Resources ........................................................................................................................ 42 Performance Efficiency Pillar ..................................................................................................... 42 Design Principles .............................................................................................................. 42 Best Practices .................................................................................................................. 43 Resources ........................................................................................................................ 45 Cost Optimization Pillar ............................................................................................................ 45 iii Machine Learning Lens AWS Well-Architected Framework Design Principles .............................................................................................................. 45 Best Practices .................................................................................................................. 46 Resources ........................................................................................................................ 50 Conclusion ....................................................................................................................................... 51 Contributors .................................................................................................................................... 52 Further Reading ............................................................................................................................... 53 Document Revisions .......................................................................................................................... 54 Notices ............................................................................................................................................ 55 iv Machine Learning Lens AWS Well-Architected Framework This whitepaper is in the process of being updated. v Machine Learning Lens AWS Well-Architected Framework Abstract Machine Learning Lens - AWS Well- Architected Framework Publication date: April 2020 (Document Revisions (p. 54)) Abstract This document describes the Machine Learning Lens for the AWS Well-Architected Framework. The document includes common machine learning (ML) scenarios and identifies key elements to ensure that your workloads are