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Empower your generative AI application with a comprehensive custom observability solution

AWS Machine Learning

Observability empowers you to proactively monitor and analyze your generative AI applications, and evaluation helps you collect feedback, refine models, and enhance output quality. In the context of Amazon Bedrock , observability and evaluation become even more crucial.

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Principal Financial Group uses QnABot on AWS and Amazon Q Business to enhance workforce productivity with generative AI

AWS Machine Learning

Principal wanted to use existing internal FAQs, documentation, and unstructured data and build an intelligent chatbot that could provide quick access to the right information for different roles. QnABot is a multilanguage, multichannel conversational interface (chatbot) that responds to customers’ questions, answers, and feedback.

Chatbots 113
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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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Track LLM model evaluation using Amazon SageMaker managed MLflow and FMEval

AWS Machine Learning

Rigorous testing allows us to understand an LLMs capabilities, limitations, and potential biases, and provide actionable feedback to identify and mitigate risk. It functions as a standalone HTTP server that provides various REST API endpoints for monitoring, recording, and visualizing experiment runs.

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Knowledge Bases in Amazon Bedrock now simplifies asking questions on a single document

AWS Machine Learning

Today, we’re introducing the new capability to chat with your document with zero setup in Knowledge Bases for Amazon Bedrock. With this new capability, you can securely ask questions on single documents, without the overhead of setting up a vector database or ingesting data, making it effortless for businesses to use their enterprise data.

APIs 125
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Generate training data and cost-effectively train categorical models with Amazon Bedrock

AWS Machine Learning

Lets say the task at hand is to predict the root cause categories (Customer Education, Feature Request, Software Defect, Documentation Improvement, Security Awareness, and Billing Inquiry) for customer support cases. We suggest consulting LLM prompt engineering documentation such as Anthropic prompt engineering for experiments.

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Pixtral-12B-2409 is now available on Amazon Bedrock Marketplace

AWS Machine Learning

Designed for both image and document comprehension, Pixtral demonstrates advanced capabilities in vision-related tasks, including chart and figure interpretation, document question answering, multimodal reasoning, and instruction followingseveral of which are illustrated with examples later in this post.