Dynamic Talks | Fine-tuning of large language models for building non-chat experiences скачать в хорошем качестве

Dynamic Talks | Fine-tuning of large language models for building non-chat experiences

Transform your AI applications beyond simple chatbots with expert LLM fine-tuning and distillation techniques! In this comprehensive Dynamic Talks session, Anil Kumar Turaga, Senior AI Engineer at Grid Dynamics, reveals practical strategies for creating specialized AI experiences that deliver real business value. What you'll learn: • Creating high-quality datasets for effective LLM fine-tuning with proper citations • Implementing cursor tap predictions and multi-step prediction systems • Building powerful non-chat AI applications (document analyzers, specialized search, content generators) • Comprehensive evaluation frameworks with automated white tests • Distillation methods to reduce model size by up to 65% while maintaining 90%+ performance • Data transformation pipelines for domain-specific model training Perfect for: AI Engineers and Data Scientists ML Engineers working with LLMs Technical Leaders planning AI implementations DevOps Engineers integrating AI systems Product Managers overseeing AI projects Key takeaways: Practical code examples and implementation strategies Battle-tested approaches used in production environments Systematic evaluation methodologies for fine-tuned models Best practices for balancing model performance and efficiency Resources mentioned: https://docs.google.com/document/d/1j... Subscribe for more expert sessions on AI, ML, and cutting-edge technology topics! Learn more about Grid Dynamics: https://www.griddynamics.com/ Connect with us on LinkedIn:   / grid-dynamics