A model that runs in a notebook serves one request at a time. A production endpoint receives many requests at once, and each one needs GPU memory for its key-value (KV) cache, a slot in the batch, and a response stream. Plain PyTorch or Hugging Face Transformers can serve the model, but you have to
Sharon Davis receives research grant to keep clinical AI models accurate
In medicine, predictive models derived from artificial intelligence (AI) and machine learning have some related weaknesses that call for monitoring and correction. Model performance can degrade over time as clinical practice, information systems and patient populations change — a problem known as model drift.
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