Model Serving

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Model serving is the stage where a trained AI model is moved into a real system so it can be used by actual applications and users. Instead of staying in a development environment, the model is deployed behind a service (often an API) that receives input and sends back a prediction. This can happen instantly for tasks like recommendations or fraud checks, or in scheduled batches for things like daily reports.

A reliable serving setup keeps the model responsive under different workloads and makes sure it doesn’t fail silently. It scales when needed, records important events, and provides clear ways to detect and fix issues. The model can be served from the cloud, from on-premise servers, or from edge devices, depending on the use case.

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