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Security best practices to consider while fine-tuning models in Amazon Bedrock

AWS Machine Learning

In this post, we delve into the essential security best practices that organizations should consider when fine-tuning generative AI models. This VPC endpoint security group only allows traffic originating from the security group attached to your VPC private subnets, adding a layer of protection. For VPC , choose your VPC.

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Build a multi-tenant generative AI environment for your enterprise on AWS

AWS Machine Learning

It also uses a number of other AWS services such as Amazon API Gateway , AWS Lambda , and Amazon SageMaker. Some components are categorized in groups based on the type of functionality they exhibit. The component groups are as follows. API Gateway is serverless and hence automatically scales with traffic.

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Secure a generative AI assistant with OWASP Top 10 mitigation

AWS Machine Learning

These steps might involve both the use of an LLM and external data sources and APIs. Agent plugin controller This component is responsible for the API integration to external data sources and APIs. The LLM agent is an orchestrator of a set of steps that might be necessary to complete the desired request.

APIs 108
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Considerations for addressing the core dimensions of responsible AI for Amazon Bedrock applications

AWS Machine Learning

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 through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.

APIs 109
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Using natural language in Amazon Q Business: From searching and creating ServiceNow incidents and knowledge articles to generating insights

AWS Machine Learning

Although were using admin privileges for the purpose of this post, its a security best practice to apply least privilege permissions and grant only the permissions required to perform a task. This involves creating an OAuth API endpoint in ServiceNow and using the web experience URL from Amazon Q Business as the callback URL.

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Streamline workflow orchestration of a system of enterprise APIs using chaining with Amazon Bedrock Agents

AWS Machine Learning

Intricate workflows that require dynamic and complex API orchestration can often be complex to manage. In this post, we explore how chaining domain-specific agents using Amazon Bedrock Agents can transform a system of complex API interactions into streamlined, adaptive workflows, empowering your business to operate with agility and precision.

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Best practices for building robust generative AI applications with Amazon Bedrock Agents – Part 1

AWS Machine Learning

This two-part series explores best practices for building generative AI applications using Amazon Bedrock Agents. This data provides a benchmark for expected agent behavior, including the interaction with existing APIs, knowledge bases, and guardrails connected with the agent. None What is the balance for the account 1234?