Remove Events Remove Natural Language Processing Remove System Architecture
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Creating asynchronous AI agents with Amazon Bedrock

AWS Machine Learning Blog

The emergence of generative AI agents in recent years has contributed to the transformation of the AI landscape, driven by advances in large language models (LLMs) and natural language processing (NLP). In this approach, the workflow emerges from the collective behavior of the agents reacting to events asynchronously.

AI 100
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Innovating at speed: BMW’s generative AI solution for cloud incident analysis

AWS Machine Learning Blog

It requires checking many systems and teams, many of which might be failing, because theyre interdependent. Developers need to reason about the system architecture, form hypotheses, and follow the chain of components until they have located the one that is the culprit.

AWS 120
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Transforming financial analysis with CreditAI on Amazon Bedrock: Octus’s journey with AWS

AWS Machine Learning Blog

Solution overview The following figure illustrates our system architecture for CreditAI on AWS, with two key paths: the document ingestion and content extraction workflow, and the Q&A workflow for live user query response. This event-driven architecture provides immediate processing of new documents.

AWS 115
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Build verifiable explainability into financial services workflows with Automated Reasoning checks for Amazon Bedrock Guardrails

AWS Machine Learning Blog

Automated Reasoning cant predict future events or handle ambiguous situations, nor can it learn from new data such as ML models. Claims processing is another fundamental function within insurance companies, and its the process used by policy holders to exercise their policy to get compensation for an event (a car accident, for example).

AWS 91
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A Guide to LLMOps: Large Language Model Operations

Heartbeat

Large language models have emerged as ground-breaking technologies with revolutionary potential in the fast-developing fields of artificial intelligence (AI) and natural language processing (NLP). Deployment : The adapted LLM is integrated into this stage's planned application or system architecture.

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How Q4 Inc. used Amazon Bedrock, RAG, and SQLDatabaseChain to address numerical and structured dataset challenges building their Q&A chatbot

Flipboard

The Q4 Platform facilitates interactions across the capital markets through IR website products, virtual events solutions, engagement analytics, investor relations Customer Relationship Management (CRM), shareholder and market analysis, surveillance, and ESG tools. Use case overview Q4 Inc.,

SQL 167
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Mitigating risk: AWS backbone network traffic prediction using GraphStorm

Flipboard

We train GNN models with historical demand and traffic data, along with other features (network incidents and maintenance events) by following the sliding-window method. Since 2020, he has focused on impact reduction and risk management in networking software systems and operations research in networking operations teams.

AWS 139