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Automate chatbot for document and data retrieval using Agents and Knowledge Bases for Amazon Bedrock

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, 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.

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Connect to Amazon services using AWS PrivateLink in Amazon SageMaker

AWS Machine Learning

This isolates the instance from the internet and makes API calls to other AWS services not possible. Test the connection You can test the connection to Amazon Bedrock using a simple Python API call. AWS PrivateLink enables you to connect privately to several AWS services, for a current list please see this page.

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Enterprise Contact Center Solutions: A Game Changer

NobelBiz

RELATED ARTICLE How to Report and Analyze Like a Pro: 10 Best Practices for Reporting and Analytics in a Contact Center Workforce Management Tools Sophisticated workforce management tools optimize agent scheduling, forecasting, and performance tracking, ensuring optimal resource utilization and productivity.

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Your guide to generative AI and ML at AWS re:Invent 2023

AWS Machine Learning

In this innovation talk, hear how the largest industries, from healthcare and financial services to automotive and media and entertainment, are using generative AI to drive outcomes for their customers. In this talk, explore strategies for putting your proprietary datasets to work when building unique, differentiated generative AI solutions.

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Build a solution for a computer vision skin lesion classifier using Amazon SageMaker Pipelines

AWS Machine Learning

AWS offers a pre-trained and fully managed AWS AI service called Amazon Rekognition that can be integrated into computer vision applications using API calls and require no ML experience. You just have to provide an image to the Amazon Rekognition API and it can identify the required objects according to pre-defined labels.

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Best prompting practices for using Meta Llama 3 with Amazon SageMaker JumpStart

AWS Machine Learning

In this post, we dive into the best practices and techniques for prompting Meta Llama 3 using Amazon SageMaker JumpStart to generate high-quality, relevant outputs. Meta Llama 3 inference parameters For Meta Llama 3, the Messages API allows you to interact with the model in a conversational way.

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Automate caption creation and search for images at enterprise scale using generative AI and Amazon Kendra

AWS Machine Learning

After ingestion, images can be searched via the Amazon Kendra search console, API, or SDK. You can then search for images using natural language queries, such as “Find images of red roses” or “Show me pictures of dogs playing in the park,” through the Amazon Kendra console, SDK, or API.