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Build generative AI applications quickly with Amazon Bedrock IDE in Amazon SageMaker Unified Studio

AWS Machine Learning

SageMaker Unified Studio setup SageMaker Unified Studio is a browser-based web application where you can use all your data and tools for analytics and AI. This will provision the backend infrastructure and services that the sales analytics application will rely on. You’ll use this file when setting up your function to query sales data.

APIs 104
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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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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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42 Best Customer Feedback Software for 2022

ProProfs Blog

Yes, you can collect their feedback on your brand offerings with simple questions like: Are you happy with our products or services? Various customer feedback tools help you track your customers’ pulse consistently. What Is a Customer Feedback Tool. Read more: 12 Channels to Capture Customer Feedback. Here we go!

Feedback 148
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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

QnABot is a multilanguage, multichannel conversational interface (chatbot) that responds to customers’ questions, answers, and feedback. Usability and continual improvement were top priorities, and Principal enhanced the standard user feedback from QnABot to gain input from end-users on answer accuracy, outdated content, and relevance.

Chatbots 115
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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. SageMaker is a data, analytics, and AI/ML platform, which we will use in conjunction with FMEval to streamline the evaluation process.

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Reducing hallucinations in LLM agents with a verified semantic cache using Amazon Bedrock Knowledge Bases

AWS Machine Learning

Solution overview Our solution implements a verified semantic cache using the Amazon Bedrock Knowledge Bases Retrieve API to reduce hallucinations in LLM responses while simultaneously improving latency and reducing costs. The function checks the semantic cache (Amazon Bedrock Knowledge Bases) using the Retrieve API.