Monitoring machine learning (ML) predictions can help improve the quality of deployed models. Capturing the data from inferences made in production can enable you to monitor your deployed models and detect deviations in model quality. Early and proactive detection of these deviations enables you to take corrective actions, such as retraining models, auditing upstream systems, or fixing quality issues.
AWS Lambda is a serverless compute service that can provide real-time ML inference at scale. In this post, we demonstrate a sample data capture feature that can be deployed to a Lambda ML inference workload.
In December 2020, Lambda introduced support for container images as a packaging format. This feature increased the deployment package size limit from 500 MB to 10 GB. Prior to this feature launch, the package size constraint made it difficult to deploy ML frameworks like TensorFlow or PyTorch to Lambda functions. After the launch, the increased package

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