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Consider the following picture, which is an AWS view of the a16z emerging application stack for large language models (LLMs). This pipeline could be a batch pipeline if you prepare contextual data in advance, or a low-latency pipeline if you’re incorporating new contextual data on the fly.
In this post, we show you how SnapLogic , an AWS customer, used Amazon Bedrock to power their SnapGPT product through automated creation of these complex DSL artifacts from human language. SnapLogic background SnapLogic is an AWS customer on a mission to bring enterprise automation to the world.
Scaling and load balancing The gateway can handle load balancing across different servers, model instances, or AWS Regions so that applications remain responsive. The AWS Solutions Library offers solution guidance to set up a multi-provider generative AI gateway. Leave us a comment and we will be glad to collaborate.
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