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Implementing safety guardrails for applications using Amazon SageMaker
- Large Language Models (LLMs) require robust safety guardrails to prevent inappropriate outputs and protect user interactions, addressing concerns like generating malicious content and protecting sensitive information.
- Implementing safety measures on Amazon SageMaker AI involves pre-deployment interventions and runtime interventions for AI safety.
- Pre-deployment interventions embed safety principles into models during training and include model guardrails as examples.
- Runtime interventions involve active safety monitoring during model operation, such as prompt engineering and output filtering.
- Combining protection layers from built-in guardrails to external safety models creates comprehensive AI safety systems.
- Built-in model guardrails provide the first line of defense, equipped with sophisticated safety features implemented during pre-training and fine-tuning phases.
- Amazon SageMaker JumpStart offers models like Meta Llama 3 with red teaming and specialized testing for critical risks.
- Amazon Bedrock Guardrails ApplyGuardrail API offers runtime safeguards based on predefined validation rules to protect against sensitive information and inappropriate content.
- Implementing Amazon Bedrock Guardrails with a SageMaker endpoint involves creating guardrails, evaluating content, and processing inputs and outputs.
- Foundation models like Llama Guard provide detailed safety checks beyond rule-based approaches, evaluating content safety in multiple languages and hazard categories.
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