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However, scaling up generative AI and making adoption easier for different lines of businesses (LOBs) comes with challenges around making sure data privacy and security, legal, compliance, and operational complexities are governed on an organizational level. In this post, we discuss how to address these challenges holistically.
The solution uses the FMs tool use capabilities, accessed through the Amazon Bedrock Converse API. This enables the FMs to not just process text, but to actively engage with various external tools and APIs to perform complex document analysis tasks. For more details on how tool use works, refer to The complete tool use workflow.
Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon with a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.
Amazon SageMaker provides purpose-built tools for machine learning operations (MLOps) to help automate and standardize processes across the ML lifecycle. All these capabilities are built to help multiple lines of business innovate with speed and agility while governing at scale with central controls.
Also learn how prompts can be integrated with your architecture and how to use API parameters for tuning the model parameters using Amazon Bedrock. This chalk talk provides an introduction to best practices for risk assessment related to fairness, robustness, explainability, privacy and security, transparency, and governance.
Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon via a single API. This improves efficiency and allows larger contexts to be used. This supports safer adoption.
Consider your security posture, governance, and operational excellence when assessing overall readiness to develop generative AI with LLMs and your organizational resiliency to any potential impacts. Many AWS customers align to industrystandard frameworks, such as the NIST Cybersecurity Framework.
In this comprehensive article, we delve into the details of call center compliance , exploring its significance, the laws and regulations governing it, common mistakes to avoid, and best practices for ensuring adherence. Seamlessly integrate proprietary or third-party CRM applications with our extensive APIs and data dictionary libraries.
The feature supports both interactive testing through the Amazon Bedrock console and automated testing through API integrations, with the ability to export test cases in JSON format for integration into continuous testing pipelines or documentation workflows. This integration enables automated validation of generative AI outputs.
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