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FedML-AWS for Amazon Sagemaker

Last updated on by s-krishnamoorthy

FedML-AWS provides end-to-end integraton for training models in Amazon Sagemaker using live business data from SAP systems and eliminates the need for duplicating the data. With only few lines of code, fedml-aws enables

  • Data discovery
  • Model training
  • Model deployment, both in Sagemaker and SAP BTP, all while enabling instant access to source business data from SAP systems.

Architecture​

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Solution Diagram Resources
You can download the Solution Diagram as a .drawio file for offline use. Alternatively, you may view and edit the Solution Diagram directly on draw.io.
Please note that any changes made online will need to be saved locally if you wish to keep them.
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Resources

Flow​

FedML, the Python Library is imported directly into Amazon Sagemaker notebook instances. FedML connects to SAP Datasphere via secure Python/SQLDBC connectivity and helps federate the critical business data needed for training models in Amazon Sagemaker.

Models trained in Amazon Sagemaker can also be optionally deployed in SAP AI Core for inferencing via FedML-AWS's seamless deployment integration.

When to use​

  1. When a customer already has AWS as part of their cloud platform strategy, where Amazon Sagemaker is their data science platform for machine learning projects.
  2. Majority of training data resides in AWS storages, with critical SAP data from various SAP applications (with semantics intact) is still needed for training.
  3. Trained models have potential to be deployed in SAP BTP Kyma for quick inferencing that involve SAP data.

Resources​