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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. Security – The solution uses AWS services and adheres to AWS Cloud Security best practices so your data remains within your AWS account.

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Revolutionizing clinical trials with the power of voice and AI

AWS Machine Learning

Site monitors conduct on-site visits, interview personnel, and verify documentation to assess adherence to protocols and regulatory requirements. However, this process can be time-consuming and prone to errors, particularly when dealing with extensive audio recordings and voluminous documentation.

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Evaluate RAG responses with Amazon Bedrock, LlamaIndex and RAGAS

AWS Machine Learning

Optimized for search and retrieval, it streamlines querying LLMs and retrieving documents. Build sample RAG Documents are segmented into chunks and stored in an Amazon Bedrock Knowledge Bases (Steps 24). For this purpose, LangChain provides a WebBaseLoader object to load text from HTML webpages into a document format.

Metrics 117
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Elevate healthcare interaction and documentation with Amazon Bedrock and Amazon Transcribe using Live Meeting Assistant

AWS Machine Learning

Today, physicians spend about 49% of their workday documenting clinical visits, which impacts physician productivity and patient care. By using the solution, clinicians don’t need to spend additional hours documenting patient encounters. This blog post focuses on the Amazon Transcribe LMA solution for the healthcare domain.

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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.

Education 112
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Amazon Bedrock Flows is now generally available with enhanced safety and traceability

AWS Machine Learning

Prerequisites Before implementing the new capabilities, make sure that you have the following: An AWS account In Amazon Bedrock: Create and test your base prompts for customer service interactions in Prompt Management. Set up your knowledge base with relevant customer service documentation, FAQs, and product information.

APIs 123
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Build generative AI applications quickly with Amazon Bedrock IDE in Amazon SageMaker Unified Studio

AWS Machine Learning

Prerequisites Before creating your application in Amazon Bedrock IDE, you’ll need to set up a few resources in your AWS account. Expected response: Based on the customer reviews and feedback, the sentiment surrounding our Office Supplies products is mixed. Prompt 2: Which 3 item types account for our most units sold?

APIs 108