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Build generative AI applications quickly with Amazon Bedrock IDE in Amazon SageMaker Unified Studio

AWS Machine Learning

SageMaker Unified Studio setup SageMaker Unified Studio is a browser-based web application where you can use all your data and tools for analytics and AI. Building a generative AI application SageMaker Unified Studio offers tools to discover and build with generative AI. To get started, you need to build a project.

APIs 107
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Build a video insights and summarization engine using generative AI with Amazon Bedrock

AWS Machine Learning

Professionals in a wide variety of industries have adopted digital video conferencing tools as part of their regular meetings with suppliers, colleagues, and customers. For integration between services, we use API Gateway as an event trigger for our Lambda function, and DynamoDB as a highly scalable database to store our customer details.

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Integrate generative AI capabilities into Microsoft Office using Amazon Bedrock

AWS Machine Learning

By harnessing the latest advancements in generative AI, we empower employees to unlock new levels of efficiency and creativity within the tools they already use every day. Note that these APIs use objects as namespaces, alleviating the need for explicit imports. If required, this setup can also be defined in Infrastructure as Code.

APIs 114
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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. API Gateway is serverless and hence automatically scales with traffic. API Gateway also provides a WebSocket API. Incoming requests to the gateway go through this point.

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Discover insights from Gmail using the Gmail connector for Amazon Q Business

AWS Machine Learning

Gmail for business is part of Google Workspace , which provides a set of productivity and collaboration tools like Google Drive , Gmail , and Google Calendar. This tool aims to make employees work smarter, move faster, and drive more significant impact by providing immediate and relevant information and streamlining tasks.

APIs 120
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Transforming credit decisions using generative AI with Rich Data Co and AWS

AWS Machine Learning

This strategy equipped us to align each task with the most suitable foundation model (FM) and tools. Its equipped with the appropriate FM for the task and the necessary tools to perform actions and access knowledge. Tools Tools extend agent capabilities beyond the FM.

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Streamline RAG applications with intelligent metadata filtering using Amazon Bedrock

AWS Machine Learning

This approach, which we call intelligent metadata filtering, uses tool use (also known as function calling ) to dynamically extract metadata filters from natural language queries. Function calling allows LLMs to interact with external tools or functions, enhancing their ability to process and respond to complex queries.