An ML engineer needs to use Amazon SageMaker to fine-tune a large language model (LLM) for text summarization. The ML engineer must follow a low-code no-code (LCNC) approach.
Which solution will meet these requirements?
A.
Use SageMaker Studio to fine-tune an LLM that is deployed on Amazon EC2 instances.
B.
Use SageMaker Autopilot to fine-tune an LLM that is deployed by a custom API endpoint.
C.
Use SageMaker Autopilot to fine-tune an LLM that is deployed on Amazon EC2 instances.
D.
Use SageMaker Autopilot to fine-tune an LLM that is deployed by SageMaker JumpStart.
SageMaker JumpStart provides access to pre-trained models, including large language models (LLMs), which can be easily deployed and fine-tuned with a low-code/no-code (LCNC) approach. Using SageMaker Autopilot with JumpStart simplifies the fine-tuning process by automating model optimization and reducing the need for extensive coding, making it the ideal solution for this requirement.
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