Remove APIs Remove Chatbots Remove Scripts
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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning

Customers can use the SageMaker Studio UI or APIs to specify the SageMaker Model Registry model to be shared and grant access to specific AWS accounts or to everyone in the organization. We will start by using the SageMaker Studio UI and then by using APIs.

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Build a contextual chatbot application using Knowledge Bases for Amazon Bedrock

AWS Machine Learning

Modern chatbots can serve as digital agents, providing a new avenue for delivering 24/7 customer service and support across many industries. Chatbots also offer valuable data-driven insights into customer behavior while scaling effortlessly as the user base grows; therefore, they present a cost-effective solution for engaging customers.

Chatbots 122
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Integrate dynamic web content in your generative AI application using a web search API and Amazon Bedrock Agents

AWS Machine Learning

Amazon Bedrock agents use LLMs to break down tasks, interact dynamically with users, run actions through API calls, and augment knowledge using Amazon Bedrock Knowledge Bases. In this post, we demonstrate how to use Amazon Bedrock Agents with a web search API to integrate dynamic web content in your generative AI application.

APIs 106
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Enhancing LLM Capabilities with NeMo Guardrails on Amazon SageMaker JumpStart

AWS Machine Learning

For a retail chatbot like AnyCompany Pet Supplies AI assistant, guardrails help make sure that the AI collects the information needed to serve the customer, provides accurate product information, maintains a consistent brand voice, and integrates with the surrounding services supporting to perform actions on behalf of the user. The Llama 3.1

Chatbots 100
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From RAG to fabric: Lessons learned from building real-world RAGs at GenAIIC – Part 1

AWS Machine Learning

Since the inception of AWS GenAIIC in May 2023, we have witnessed high customer demand for chatbots that can extract information and generate insights from massive and often heterogeneous knowledge bases. Implementation on AWS A RAG chatbot can be set up in a matter of minutes using Amazon Bedrock Knowledge Bases. doc,pdf, or.txt).

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GPT-NeoXT-Chat-Base-20B foundation model for chatbot applications is now available on Amazon SageMaker

AWS Machine Learning

This demonstration provides an open-source foundation model chatbot for use within your application. As a JumpStart model hub customer, you get improved performance without having to maintain the model script outside of the SageMaker SDK. The inference script is prepacked with the model artifact.

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Build a serverless voice-based contextual chatbot for people with disabilities using Amazon Bedrock

AWS Machine Learning

Specifically, we focus on chatbots. Chatbots are no longer a niche technology. Although AI chatbots have been around for years, recent advances of large language models (LLMs) like generative AI have enabled more natural conversations. We also provide a sample chatbot application. We discuss this later in the post.