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FedML-Azure for Azure ML Training on Live SAP Data

Last updated on by s-krishnamoorthy

This content has been archived

This content has been archived. It describes a retired or obsolete approach and is no longer maintained or supported by SAP. It remains available for reference only and should not be relied on as current guidance.

Joint Reference Architecture

This content was developed in collaboration with Microsoft.

FedML-Azure provides end-to-end integration for training models in Azure Machine Learning service, using live business data from SAP systems and eliminates the need for duplicating the data. With only few lines of code, fedml-azure enables

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

Architecture​

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Resources

Flow​

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

Models trained in Azure ML can also be optionally deployed in SAP BTP Kyma for inferencing via FedML-Azure's seamless deployment integration.

When to use​

  1. When a customer already has Microsoft Azure service as part of their cloud platform strategy, and have invested in using Azure ML is their data science platform for machine learning projects.
  2. Majority of training (non-SAP) data resides in the Azure platform 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​