Why the Treasure Hunt Demo Broke Every Query Tool We Fed It
The article discusses the challenges faced while integrating AI inference into a data warehouse environment. The initial approach using ONNX Runtime for intent modeling resulted in high latency and memory issues. A revised architecture using AWS Lambda and Snowpark Container Services improved performance and reduced errors significantly.
- ▪The initial implementation with ONNX Runtime led to latency issues and memory leaks, causing operational disruptions.
- ▪The new architecture writes raw event JSON to S3, where a Lambda function tokenizes the data before loading it into Snowflake.
- ▪Post-implementation, latency improved to a steady 185-205 ms, and NULL intent rows dropped from 7% to 0.18%.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3942477) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Lisa Zulu Posted on May 30 Why the Treasure Hunt Demo Broke Every Query Tool We Fed It #webdev #programming #ai #machinelearning The Problem We Were Actually Solving We were not building a demo. We needed to let Veltrix operators run A/B experiments on synthetic user journeys without melting the underlying SQL warehouse.
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