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7 Agentic RAG System Architectures to Build AI Agents

Analytics Vidhya

In this quest to learn about LLMs, RAG and more, I discovered the potential of AI Agentsautonomous systems capable of executing tasks and making decisions with minimal human intervention. Going back to […] The post 7 Agentic RAG System Architectures to Build AI Agents appeared first on Analytics Vidhya.

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Why Microsoft is outspending big tech on Nvidia AI chips

Dataconomy

Building comprehensive AI infrastructure encompasses not only robust processing power but also integrated storage components and software layers, highlighting complexities in the systems architecture. Featured image credit: Sam Torres/Unsplash

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Algorithms for Modern Processor Architectures

Hacker News

We advocate for a comprehensive approach: robust mathematical models grounded in a current and detailed understanding of system architecture. Through this lens, we explore how algorithmic design can leverage these characteristics of contemporary processors, drawing insights from practical case studies in widely used software.

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Create a multimodal chatbot tailored to your unique dataset with Amazon Bedrock FMs

AWS Machine Learning Blog

The following system architecture represents the logic flow when a user uploads an image, asks a question, and receives a text response grounded by the text dataset stored in OpenSearch.

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Fraud detection empowered by federated learning with the Flower framework on Amazon SageMaker AI

AWS Machine Learning Blog

Next steps include experimenting with these datasets, using the Flower Federated Learning Workshop for hands-on guidance, reviewing the system architecture for deeper understanding, and engaging with the AWS account team to tailor and scale your federated learning solution.

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Benefits of Using LiteLLM for Your LLM Apps

KDnuggets

However, with so many model providers out there, it becomes hard to establish a standard for LLM implementation, especially in the case of multi-model system architectures. . # Conclusion In the era of LLM product growth, it has become much easier to build LLM applications. This is why LiteLLM can help us build LLM Apps efficiently.

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Data integration

Dataconomy

Documentation of data architecture Thorough documentation of data systems architecture is crucial for effective integration and long-term maintenance. Best practices for data integration Implementing best practices ensures successful data integration outcomes.