The issue of the application artifact size is definitely real and it blocks some NLP/ML workloads for sure. Consider that a today problem that isn't hard in Lam

The issue of the application artifact size is definitely real and it blocks some NLP/ML workloads for sure. Consider that a today problem that isn't hard in Lambda. But we've 100% got customers doing near realtime streaming analytics in complicated pipelines feeding off of things like Kinesis Data Streams. This FINRA example is one datapoint: https://aws.amazon.com/solutions/case-studies/finra-data-validation/ https://aws.amazon.com/solutions/case-studies/finra-data-val... and this Thompson Reuters one: https://aws.amazon.com/solutions/case-studies/thomson-reuters/ https://aws.amazon.com/solutions/case-studies/thomson-reuter... These are nontrivial and business critical workloads. Thanks, - Chris Munns - AWS - Serverless - https://twitter.com/chrismunns https://twitter.com/chrismunns edit: ------------------------------------- Missosoup i see you making changes to your comment and it greatly changes the tone/context. i won't adjust my own reply in suit but leave it as it was for your original comments on this.