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

AWS Machine Learning

We recently announced the general availability of cross-account sharing of Amazon SageMaker Model Registry using AWS Resource Access Manager (AWS RAM) , making it easier to securely share and discover machine learning (ML) models across your AWS accounts. Mitigation strategies : Implementing measures to minimize or eliminate risks.

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Enhancing LLM Capabilities with NeMo Guardrails on Amazon SageMaker JumpStart

AWS Machine Learning

Some links for security best practices are shared below but we strongly recommend reaching out to your account team for detailed guidance and to discuss the appropriate security architecture needed for a secure and compliant deployment. When a user asks about pets, the chatbot will provide an answer. What is Nemo Guardrails?

Chatbots 116
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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 137
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Transforming Customer Experience with Contact Center Automation

CCNG

Increases Engagement: Automation enables faster response times, personalized interactions, and 24/7 availability through self-service options such as chatbots and interactive voice response (IVR) systems. Conversational Self-Service: Conversational AI goes beyond scripted interactions, offering intuitive self-service options.

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6 Killer Applications for Artificial Intelligence in the Customer Engagement Contact Center

If Artificial Intelligence for businesses is a red-hot topic in C-suites, AI for customer engagement and contact center customer service is white hot. This white paper covers specific areas in this domain that offer potential for transformational ROI, and a fast, zero-risk way to innovate with AI.

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Build generative AI agents with Amazon Bedrock, Amazon DynamoDB, Amazon Kendra, Amazon Lex, and LangChain

AWS Machine Learning

The agent can assist users with finding their account information, completing a loan application, or answering natural language questions while also citing sources for the provided answers. The web channel includes an Amplify hosted website with an Amazon Lex embedded chatbot for a fictitious customer.

Scripts 128
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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.