Every organization has access to the same foundation models. The real competitive advantage comes from customizing them with your proprietary data and domain expertise. But getting there is complex, even for experienced teams. It requires mastering fine-tuning techniques like Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Reinforcement Learning Verifiable Rewards (RLVR), navigating fragmented APIs and model-specific data formats, designing rigorous evaluations, and managing months-long experiment cycles.
This is a companion discussion topic for the original entry at https://aws.amazon.com/blogs/machine-learning/agent-guided-workflows-to-accelerate-model-customization-in-amazon-sagemaker-ai/