Fine-Tuning and Adapting Foundation Models

Customizing pre-trained foundation models is the key to unlocking their value for specific business needs and domain-specific tasks. This text-based course introduces you to the essential methodologies for adapting large-scale models without the massive costs of training from scratch. You will progress from understanding core post-training concepts to evaluating and deploying customized models, gaining the practical knowledge required to choose and implement the right adaptation strategy for your projects. What you will learn: * Understand the fundamentals of transfer learning and the post-training lifecycle * Explore parameter-efficient fine-tuning (PEFT) techniques including LoRA and prefix tuning * Learn how reinforcement learning from human feedback (RLHF) and direct preference optimization (DPO) align models with human values * Apply dataset preparation best practices for specialized domain adaptation * Evaluate fine-tuned model performance, drift, and bias using modern benchmarking tools. The course begins with foundational concepts and terminology, establishing a solid theoretical base before guiding you through step-by-step written explanations of modern adaptation workflows, alignment strategies, and evaluation methodologies. This course is designed for software developers, data practitioners, and tech enthusiasts who are new to model customization and want to learn the fundamentals without complex prerequisites. Start reading today to master the art of foundation model adaptation.