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Use RAG for drug discovery with Knowledge Bases for Amazon Bedrock

AWS Machine Learning - AI

Knowledge Bases for Amazon Bedrock allows you to build performant and customized Retrieval Augmented Generation (RAG) applications on top of AWS and third-party vector stores using both AWS and third-party models. If you want more control, Knowledge Bases lets you control the chunking strategy through a set of preconfigured options.

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Automate the insurance claim lifecycle using Agents and Knowledge Bases for Amazon Bedrock

AWS Machine Learning - AI

You can now use Agents for Amazon Bedrock and Knowledge Bases for Amazon Bedrock to configure specialized agents that seamlessly run actions based on natural language input and your organization’s data. System integration – Agents make API calls to integrated company systems to run specific actions.

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Build a contextual chatbot application using Knowledge Bases for Amazon Bedrock

AWS Machine Learning - AI

One way to enable more contextual conversations is by linking the chatbot to internal knowledge bases and information systems. Integrating proprietary enterprise data from internal knowledge bases enables chatbots to contextualize their responses to each user’s individual needs and interests.

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The importance of software documentation tools

Apiumhub

Atlassian’s Confluence is a document management system that facilitates collaboration and knowledge sharing across a variety of departments and functions. You can use Confluence to collect your team’s ideas, knowledge, and plans and then switch to Jira in order to create and track issues that are related to this information.

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Generate customized, compliant application IaC scripts for AWS Landing Zone using Amazon Bedrock

AWS Machine Learning - AI

With Amazon Bedrock, teams can input high-level architectural descriptions and use generative AI to generate a baseline configuration of Terraform scripts. AWS Landing Zone architecture in the context of cloud migration AWS Landing Zone can help you set up a secure, multi-account AWS environment based on AWS best practices.

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Metadata Management: Process, Tools, Use Cases, and Best Practices

Altexsoft

Metadata management is a set of activities, technologies, and policies that target metadata collection, storage, and organizing. Plus, a data fabric architecture design approach is also based on metadata as one of the main building blocks. Metadata storage usually implies developing a specialized repository.

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An overview of 15 commonly used SaaS applications

Openxcell

It was founded by Salesforce.com in 1999, which is frequently regarded as the first real SaaS company to provide a web-based CRM system. Earlier SaaS was primarily based on CRM and other business software. Scalability- SaaS systems are simple to scale and can easily accommodate the business’s constantly changing needs.