Large language models (LLMs) have transformed how we interact with AI, but one size doesn’t fit at all. Out-of-the-box LLMs are trained with broad, general knowledge and improved for a wide range of use cases, but they often fall short when it comes to domain-specific tasks, proprietary workflows, or unique business requirements. Enterprise customers increasingly need specialized LLMs that deeply understand their proprietary data, business processes, and domain-specific terminology. Without customization, you’re forced to choose between accepting generic responses or settling for a middle ground with excessive context engineering. Nova Customization provides a suite of features, ranging from Amazon Bedrock customization options such as Supervised Fine-Tuning (SFT) and Reinforcement Fine Tuning (RFT) to Amazon SageMaker AI customization capabilities, including SFT, Direct Preference Optimization (DPO), RFT, along with both LoRA and full rank based customization.
…
