Explore key challenges and solutions in transitioning LLM-based applications from prototype to production in this 1-hour 27-minute conference talk from the Toronto Machine Learning Series. Learn from Machine Learning Engineer Ian Yu's expertise as he addresses critical implementation aspects, including strategic alignment of LLMs with product design, empirical tips for LLM chaining, and integration of LLM with non-LLM systems. Discover practical approaches to common production challenges such as output inconsistencies at scale, system maintainability, and evaluation objectives. Gain insights into build-vs-buy decisions for components like prompt versioning, orchestration, and vector databases. Participate in hands-on demonstrations of bite-sized systems while exploring current trends and predictions in production-focused LLM development. Master the art of creating maintainable, scalable LLM applications that effectively bridge the gap between prototyping and production deployment.
From Prototype to Production: Implementing LLM-Based Applications
Toronto Machine Learning Series (TMLS) via YouTube
Overview
Syllabus
The Gap From Prototype to Production
Taught by
Toronto Machine Learning Series (TMLS)
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