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This post presents a solution where you can upload a recording of your meeting (a feature available in most modern digital communication services such as Amazon Chime ) to a centralized video insights and summarization engine. With Lambda integration, we can create a web API with an endpoint to the Lambda function.
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Its the kind of ambitious mission that excites me, not just because of its bold vision, but because of the incredible technical challenges it presents. Rahul has over twenty years of experience in technology and has co-founded two companies, one focused on analytics and the other on IP-geolocation.
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Refer to Getting started with the API to set up your environment to make Amazon Bedrock requests through the AWS API. Test the code using the native inference API for Anthropics Claude The following code uses the native inference API to send a text message to Anthropics Claude. client = boto3.client("bedrock-runtime",
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 through a unified API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.
The frontend UI interacts with the extract microservice through a RESTful interface provided by Amazon API Gateway. It offers details of the extracted video information and includes a lightweight analytics UI for dynamic LLM analysis. Detect generic objects and labels using the Amazon Rekognition label detection API.
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In the architecture shown in the following diagram, users input text in the React -based web app, which triggers Amazon API Gateway , which in turn invokes an AWS Lambda function depending on the bias in the user text. Additionally, it highlights the specific parts of your input text related to each category of bias.
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In the second post , we present the use cases and dataset to show its effectiveness in analyzing real-world healthcare datasets, such as the eICU data , which comprises a multi-center critical care database collected from over 200 hospitals. Therefore, it brings analytics to data, rather than moving data to analytics.
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But finding PII among the morass of electronic data can present challenges for an organization. This two pass solution was made possible by using the ContainsPiiEntities and DetectPiiEntities APIs. The Logikcull applications servers are able to identify the individual instances of PII by making the DetectPiiEntities API call.
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