Model Customization and Fine-Tuning on Bedrock

Standard foundation models are powerful, but adapting them to your specific industry terminology and proprietary data is what unlocks their true potential. This text-based course guides you through the essential strategies for tailoring generative AI models to your unique domain requirements. You will understand how to select and implement the right customization path on Bedrock, transitioning from generic outputs to highly specialized AI behaviors. Through clear, written explanations and conceptual step-by-step guides, you will gain the confidence to optimize model performance, manage costs, and maintain data privacy. What you'll learn: Understand the foundational differences between fine-tuning, continued pre-training, and prompt engineering; Compare parameter-efficient fine-tuning (PEFT) techniques like LoRA with full fine-tuning; Analyze model distillation methods to run highly efficient, smaller models without losing accuracy; Configure custom model imports and leverage third-party marketplace models securely; Implement smart prompt routing to balance performance, latency, and cost across multiple models; Evaluate customized models using industry-standard metrics to ensure safety and alignment. The course begins with core model customization concepts and data preparation requirements, then progresses through step-by-step written walkthroughs of Bedrock's advanced tuning features. This foundational program is designed for developers, data professionals, and AI enthusiasts who want to understand model adaptation; no prior machine learning training is required. Start reading today to unlock the full power of tailored generative AI.