What is the benefit of fine-tuning a foundation model (FM)?

Comprehensive and Detailed Explanation from AWS AI Documents:

Fine-tuning a foundation model means taking a pre-trained large model and continuing its training on domain-specific or task-specific data to specialize it for a particular use case. Fine-tuning does not retrain the FM from scratch (which would be costly and time-consuming). Instead, it improves model accuracy, relevance, and contextual adaptation for the intended application (e.g., legal, healthcare, customer support).

From AWS Docs:

“With Amazon Bedrock, you can fine-tune foundation models on your own data to specialize them for your unique use cases.”

“Fine-tuning a foundation model adapts it to a specific task by training on smaller sets of labeled data relevant to the problem domain.”

???? Reference:

AWS Documentation – Fine-tuning foundation models in Amazon Bedrock