Conclusion

The Well-Architected ML design principles in this paper provide the guidance for the best practices collection. The technology and cloud agnostic best practices across the Well-Architected pillars provide architectural guidance for each phase of the ML lifecycle. Implementation plans provide guidance on implementing these best practices on AWS.

Architecture diagrams demonstrate the lifecycle phases with the supporting technologies, that enable many of the best practices introduced in this paper. The ML lens extends the Well-Architected Framework, and builds specific machine learning best practices upon it. As you work towards building and deploying production ML workloads in AWS, we recommend reviewing the AWS Well-Architected Framework pillar best practices.

Use the lens to build ML workloads with operational excellence, security, reliability, performance efficiency, cost optimization, and sustainability in mind. Plan early and make informed decisions when designing new workloads. Use the best practices to guide you through building and deploying new workloads faster. Using the lens guidance, evaluate existing workloads regularly to identify, mitigate, and address potential issues early.