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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.

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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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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.

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10 Essential AI Tools for Multilingual Customer Support in 2025

CSM Magazine

AI-driven tools improve efficiency, accuracy, and scalability, enabling companies to handle multilingual interactions seamlessly. This article delves into the top 10 AI tools that are essential for enhancing multilingual customer support in 2025, providing insights into their functionalities, benefits, and implementation strategies.

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Track LLM model evaluation using Amazon SageMaker managed MLflow and FMEval

AWS Machine Learning

Evaluations are also a fundamental tool during application development to validate the quality of prompt templates. SageMaker is a data, analytics, and AI/ML platform, which we will use in conjunction with FMEval to streamline the evaluation process. We specifically focus on SageMaker with MLflow.

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Empower your generative AI application with a comprehensive custom observability solution

AWS Machine Learning

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies such as AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon through a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI.