As enterprise businesses embrace Machine Learning (ML) across their organisations, manual workflows for building, training, and deploying ML models tend to become bottlenecks to innovation. To overcome this, enterprises need to shape a clear operating model defining how multiple personas, such as Data Scientists, Data Engineers, ML Engineers, IT, and Business stakeholders, should collaborate and interact, how to separate the concerns, responsibilities and skills, and how to leverage AWS services optimally. This combination of ML and Operations, so-called MLOps, is helping companies streamline their end-to-end ML lifecycle and boost productivity of data scientists while maintaining high model accuracy and enhancing security and compliance.
This repository contains multiple MLOps solutions:
- mlops-multi-account-cdk: contains the CDK MLOps offering. For details on deployment, check out the README
- mlops-multi-account-tf: contains the Terraform MLOps offering README
If you have any comments or questions, please contact:
The maintaining Team:
- Viktor Malesevic malesv@amazon.de
- Ravi Bhushan Ratnakar ravibrat@amazon.de
- Marco Geiger marcogei@amazon.com
- Omar Shouman omsho@amazon.de
- Alessandro Cere alecere@amazon.com
- Raul Diaz Garcia garczrau@amazon.com
- Luca Granalli granluc@amazon.com
- Anirudh Gangwal angagwa@amazon.com
- Sokratis Kartakis kartakis@amazon.com
- Jordan Grubb jmgrubb@amazon.co.uk
- Irene Arroyo Delgado iiarroyo@amazon.es