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STADLE Deployment Scenarios

Seamlessly integrate STADLE into your environment

 Single data source

​​The model is independently trained with the new data 1, 2, 3 …

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​​Integrate STADLE API into Model Training Process in the cloud or on-premise server

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STADLE Orchestrator is to continuously train the model

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Multiple data sources (cloud or edge training)

The models are independently trained with the new data 1, 2, 3 … on the edge and in the cloud

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Integrate STADLE API into Model Training. Processes on the edge side and in the cloud 

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STADLE Orchestrator is to continuously train the models both on the edge and cloud

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Edge training and inference together

The models are independently trained in different environments creating data silos …

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Integrate STADLE API into both Model Training Processes and Deployment Processes in edge devices

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STADLE Orchestrator aggregates and deploys models all in edge environments

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Cloud training, edge deployment/inference

Manual deployment is required to update the edge device models

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Integrate STADLE API into both Model Training Processes  in the cloud and Deployment Processes in edge devices

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STADLE Orchestrator aggregates models trained in the cloud and deploys models on edge

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Subset edge training, subset edge inference

Model training processes are independent and manual deployment is required to update the edge device models

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Integrate STADLE API into both Model Training Processes both in the cloud & edge and Deployment Processes in edge devices

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STADLE Orchestrator aggregates models trained in the cloud and deploys models on some of the edge devices

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Training through “virtual decentralization”, convert to decentralized case

By effectively Integrating STADLE API into centralized Model Training Processes in the cloud, efficient training of the model is realized

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Data is dispersed into another training module to be orchestrated

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Training modules can be extended as much as you want

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