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How does Amazon Bedrock allow users to fine-tune a model for a specific business workflow such as legal analysis or medical summarization?
A
By letting users train models entirely from scratch on GPU clusters
B
By enabling LoRA-based fine-tuning using user-uploaded labeled datasets
C
By generating new model layers through auto-architecture search
D
By modifying the tokenizer to match legal or medical vocabulary
Explanation:
Amazon Bedrock allows users to fine-tune foundation models for specific business workflows using LoRA (Low-Rank Adaptation)-based fine-tuning with user-uploaded labeled datasets.
LoRA-based Fine-tuning: LoRA is a parameter-efficient fine-tuning technique that adds small, trainable layers to a pre-trained model rather than retraining the entire model. This makes fine-tuning faster and more cost-effective.
User-Uploaded Labeled Datasets: Users can upload their own labeled datasets specific to their domain (e.g., legal documents, medical reports) to train the model on their specific use case.
Why Other Options Are Incorrect:
Option A: Amazon Bedrock doesn't typically allow training models from scratch on GPU clusters; it focuses on fine-tuning existing foundation models.
Option C: Auto-architecture search is not the primary method for fine-tuning in Amazon Bedrock.
Option D: While vocabulary adaptation might be part of the process, the primary mechanism is LoRA-based fine-tuning with labeled data, not just tokenizer modification.
Business Workflow Applications: This approach enables customization for specialized domains like legal analysis (contract review, case law analysis) or medical summarization (patient record summarization, clinical note generation) without requiring extensive machine learning expertise.
Benefits:
Faster deployment of domain-specific models
Lower computational costs compared to full model training
Better performance on specialized tasks
Maintains the general knowledge of the foundation model while adapting to specific domains