Improving the Accuracy of Domain Specific Tasks with LLM Distillation

In this 49-minute webinar from Snorkel AI, Shane Johnson presents a scalable approach to LLM distillation for improving domain-specific task accuracy. Learn how to adapt large language models to real-world applications by distilling them into smaller, more efficient models that maintain high performance while reducing latency and costs. Discover techniques including weak supervision, labeling functions, and limited-data fine-tuning that help teams transition from foundation models to production-ready solutions. The presentation covers the challenges of deploying LLMs, evaluation methods for domain-specific tasks, architectural considerations for distilled models, and real enterprise case studies. Perfect for AI teams seeking to balance accuracy, speed, and resource efficiency when deploying specialized models. The webinar includes detailed segments on cost challenges, real-world AI complexity, model combinations, deployment tradeoffs, and comparative performance analysis.