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doc-exports/docs/modelarts/umn/modelarts_01_0015.html
Jiang, Beibei 781e07249c ModelArts 2021430 (GA) UMN 25072022 provided by R&D (third review) 
Reviewed-by: gtema <artem.goncharov@gmail.com>
Co-authored-by: Jiang, Beibei <beibei.jiang@t-systems.com>
Co-committed-by: Jiang, Beibei <beibei.jiang@t-systems.com>
2022-09-06 10:45:57 +00:00

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Model Deployment

Generally, AI model deployment and large-scale implementation are complex.

ModelArts resolves this issue by deploying a trained model on different devices in various scenarios with only a few clicks. This secure and reliable one-stop deployment is available for individual developers, enterprises, and device manufacturers.

Figure 1 Process of deploying a model
  • The real-time inference service features high concurrency, low latency, and elastic scaling.
  • Models can be deployed as real-time inference services and batch inference tasks.
Parent topic: Basic Knowledge