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Copy file name to clipboardExpand all lines: online-machine-learning-aws-lambda/README.md
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@@ -6,12 +6,16 @@ This project contains source code and supporting files for a serverless applicat
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This example includes the following files and folders:
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- app/lambda_inference/ - Code for the application's inference Lambda function
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- app/lambda_inference/ - Code for the application's training Lambda function
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- app/*/app.py - Code for the application's Lambda function.
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- app/*/Dockerfile - The Dockerfile to build the container image.
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- app/*/requirements.txt - The pip requirements to be installed during the container build.
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- template.yaml - A template that defines the application's AWS resources.
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- app/lambda_inference/ - Code for the application's inference Lambda function.
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- app/lambda_training/ - Code for the application's training Lambda function.
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- app/*/app.py - Code for the application's Lambda function.
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- app/*/Dockerfile - The Dockerfile to build the container image.
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- app/*/requirements.txt - The pip requirements to be installed during the container build.
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- template.yaml - A template that defines the application's AWS resources.
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- events/inference_event.json - A sample payload to test the inference Lambda function locally.
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- events/train_event.json - A sample payload to test the training Lambda function locally.
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- env.json - Json file to add the bucket name of your deployed S3 model bucket for local testing of the Lambda functions.
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- notebooks/test_notebook.ipynb - A Jupyter notebook to test the lambda function in the cloud by invoking them directly and through API Gateway.
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The application uses several AWS resources, including Lambda functions and an API Gateway API. These resources are defined in the `template.yaml` file in this project. You can update the template to add AWS resources through the same deployment process that updates your application code.
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