As a student learning data science and machine learning, studying models like NVIDIA Nemotron helps me understand how real-world AI goes beyond training accuracy — into safety, reasoning, and usability.
Still early in my journey, but excited to keep learning how large models are designed and evaluated responsibly.
Any recommended resources to better understand LLM evaluation and alignment?