Model Fine-tuning and Customization
Description
Advanced techniques for customizing and fine-tuning generative AI models to meet specific business requirements and improve performance on domain-specific tasks.
Learning Objectives
Learners will master various model customization techniques including parameter-efficient fine-tuning, full fine-tuning, reinforcement learning from human feedback (RLHF), and specialized tuning methods to adapt pre-trained models for specific use cases and business requirements.
Topics (6)
Foundation concepts of transfer learning, fine-tuning methodologies, and understanding when and how to apply different tuning strategies for optimal model performance.
Understanding and implementation of RLHF processes including reward model training, policy optimization, and human preference learning for model alignment.
Strategies for adapting general-purpose models to specific domains including data preparation, domain vocabulary integration, and specialized evaluation metrics.
Comprehensive evaluation frameworks for fine-tuned models including domain-specific metrics, benchmark selection, and validation strategies for custom use cases.
Best practices for deploying custom models including version management, A/B testing, performance monitoring, and continuous improvement processes.
Advanced techniques for fine-tuning large models with minimal computational resources using methods like Low-Rank Adaptation and other parameter-efficient approaches.