Choosing between storage mechanisms for ML inferencing with AWS Lambda

This post is written by Veda Raman, SA Serverless, Casey Gerena, Sr Lab Engineer, Dan Fox, Principal Serverless SA. For real-time machine learning inferencing, customers often have several machine learning models trained for specific use-cases. For each inference request, the model must be chosen dynamically based on the input parameters. This blog post walks through the architecture of hosting multiple machine learning models using AWS Lambda as the compute platform. There is a CDK application that allows you to try these different architectures in your own account.