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Create a generative AI–powered custom Google Chat application using Amazon Bedrock

AWS Machine Learning

The custom Google Chat app, configured for HTTP integration, sends an HTTP request to an API Gateway endpoint. Before processing the request, a Lambda authorizer function associated with the API Gateway authenticates the incoming message. The following figure illustrates the high-level design of the solution.

APIs 124
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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning

We recently announced the general availability of cross-account sharing of Amazon SageMaker Model Registry using AWS Resource Access Manager (AWS RAM) , making it easier to securely share and discover machine learning (ML) models across your AWS accounts.

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Transcribe, translate, and summarize live streams in your browser with AWS AI and generative AI services

AWS Machine Learning

Recognizing this need, we have developed a Chrome extension that harnesses the power of AWS AI and generative AI services, including Amazon Bedrock , an AWS managed service to build and scale generative AI applications with foundation models (FMs). To launch the solution in a different Region, change the aws_region parameter accordingly.

APIs 133
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Benchmarking Amazon Nova and GPT-4o models with FloTorch

AWS Machine Learning

Amazon Bedrock APIs make it straightforward to use Amazon Titan Text Embeddings V2 for embedding data. The implementation used the universal gateway provided by the FloTorch enterprise version to enable consistent API calls using the same function and to track token count and latency metrics uniformly. get("message", {}).get("content")

Benchmark 112
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Secure a generative AI assistant with OWASP Top 10 mitigation

AWS Machine Learning

Generative AI scoping framework Start by understanding where your generative AI application fits within the spectrum of managed vs. custom. These steps might involve both the use of an LLM and 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 117
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Enterprise-grade natural language to SQL generation using LLMs: Balancing accuracy, latency, and scale

AWS Machine Learning

Data across these domains is often maintained across disparate data environments (such as Amazon Aurora , Oracle, and Teradata), with each managing hundreds or perhaps thousands of tables to represent and persist business data. Depending on the use case, this can be a static or dynamically generated script.

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Build a Multi-Agent System with LangGraph and Mistral on AWS

AWS Machine Learning

By using the power of LLMs and combining them with specialized tools and APIs, agents can tackle complex, multistep tasks that were previously beyond the reach of traditional AI systems. LangGraph is essential to our solution by providing a well-organized method to define and manage the flow of information between agents.

APIs 127