A municipal archives desk wants memorial blurbs in a house voice and has a private corpus. They do not want to train a giant sequence model from random weights. Which approach fits?
Select an answer to reveal the explanation.
Short Explanation
The archives desk wants memorial blurbs in a house voice. Fine-tune a Bedrock model on the private corpus instead of raising a giant sequence model from scratch. K-Means clusters unlabeled piles, and Rekognition looks at pictures, not blurbs.
Full Explanation
Fine-tune an Amazon Bedrock model on the custom dataset when the team needs house-style generative text without training a foundation model from scratch. K-Means and Rekognition are not Bedrock fine-tunes.