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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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Accelerate threat modeling with generative AI

Flipboard

How generative AI can help Generative AI has revolutionized threat modeling by automating traditionally complex analytical tasks that required human judgment, reasoning, and expertise. Drawing from extensive security databases like MITRE ATT&CK and OWASP , these models can quickly identify potential vulnerabilities across complex systems.

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Unlocking the Power of Generative AI with Real-Time Data and Advanced Features

ODSC - Open Data Science

This challenge limits the potential of self-service analytics and delays decision-making. Architectural Design: Real-Time, Event-Driven The system architecture is built on microservices and event-driven principles: Flink AI enables real-time LLM calls, such as summarizing data or generating SQL within the stream.

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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. This may be useful for later chat assistant analytics. Step 3 – The Lambda function stores the query image in Amazon S3 with a specified ID.

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9 Careers You Could Go into With a Data Science Degree

Smart Data Collective

The typical duties and responsibilities of a data architect include ensuring data solutions are built for design analytics and performance across numerous platforms. The role could also involve finding ways to improve the functionality and performance of existing systems and providing access to database analysts and administrators.

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How we built our AI Lakehouse

AssemblyAI

The design of our AI Lakehouse is intended to efficiently manage, store, and serve large volumes of data, offering fast access and robust analytics capabilities. It not only supports application use cases like model training and benchmarking but also facilitates datasets discovery and the execution of analytical queries across all datasets.

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Announcing the First Speakers for the Virtual Agentic AI Summit in July

ODSC - Open Data Science

His current research explores the frontiers of machine intelligence, with a focus on simulation and evaluation methodologies for intelligent systems. Amy Hodler Amy Hodler is a leading voice in graph analytics and responsible AI, with decades of experience in emerging technologies.