Exploring Model Variance and Cost Efficiency in AI Applications

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This chapter explores the differences in response quality between AI applications and traditional database queries, highlighting the deterministic nature of SQL against the unpredictability of AI models. It also discusses cost reductions and enhancements in sentiment analysis as AI technology evolves.

LLMs are becoming more mature and accessible, and many teams are now integrating them into common business practices such as technical support bots, online real-time help, and other knowledge-base-related tasks. However, the high cost of maintaining AI teams and operating AI pipelines is becoming apparent.

Maxime Armstrong and Yuhan Luo are Software Engineers at Dagster, which is an open source platform for orchestrating data and AI pipelines. They join the show with Sean Falconer to talk about running cost-effective AI pipelines.

Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from information visualization to quantum computing. Currently, Sean is Head of Marketing and Developer Relations at Skyflow and host of the podcast Partially Redacted, a podcast about privacy and security engineering. You can connect with Sean on Twitter @seanfalconer.