Introduction to Serverless Model Deployment with AWS Lambda and ONNX - PyImageSearch

"This approach can be complex, resource-intensive, and costly - especially if your model is not running continuously. Serverless model deployment addresses these challenges by eliminating the need for manual server management. With a serverless architecture, your model is packaged as a function that runs only when invoked, automatically scaling up or down based on demand. This allows you to focus on your model logic rather than on infrastructure provisioning and maintenance."

"A user sends an API request (e.g., to classify an image or analyze text). The request is routed through Amazon API Gateway, which forwards it to AWS Lambda. AWS Lambda loads the AI model and performs inference using ONNX Runtime. The inference result is returned to the user via API Gateway. In Figure 1, a client application sends a request through Amazon API Gateway, which triggers an AWS Lambda function running ONNX Runtime for model inference. The response is then sent back to the client. This architecture ensures you pay only for compute time when your model runs, making it highly cost-effective and scalable."

AWS Lambda, Amazon API Gateway, and ONNX Runtime combine to enable serverless AI inference that avoids manual server provisioning and scaling. API Gateway accepts client requests and forwards them to Lambda functions, where ONNX Runtime executes Python-based models to perform inference. The serverless approach packages models as functions that run only on invocation and automatically scale with demand, reducing costs by charging only for compute time used. This architecture is especially suitable for lightweight inference tasks and for workflows where continuous model hosting would be inefficient or expensive.

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