Frontier models are the fastest way to launch an AI product, but what happens when usage…
This is a AI post classified by Jev as AI infra & evals (a free resource), kept by the AI Radar because it carries real work, not commentary.
Frontier models are the fastest way to launch an AI product, but what happens when usage scales? In our latest case study, @Shopify shares how they built a continual learning loop using @PyTorch and @vllm_project for their GraphQL agent, turning everyday production failures directly into model weight improvements. Read the full breakdown by Cody Mazza-Anthony and @Drewch to learn more about they define quality, calibrate judges, and distribute training across GPUs: https://pytorch.org/blog/how-shopify-built-a-continual-learning-loop-with-pytorch-and-vllm/ Plus, don't miss the @ShopifyEng key
Posted by PyTorch (513.3k followers) 1 h ago · 14 likes · 2.6k views · view the original post on X. Kept by the AI Radar as AI infra & evals.
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