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Small Language Models, Big Impact: Fine-Tuning DistilGPT-2 for Medical Queries

Analytics Vidhya

Language models have transformed how we interact with data, enabling applications like chatbots, sentiment analysis, and even automated content generation. However, most discussions revolve around large-scale models like GPT-3 or GPT-4, which require significant computational resources and vast datasets.

Modeling 208
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A Practical Guide to Multimodal Data Analytics

KDnuggets

BigQuery's ObjectRef unifies structured and unstructured data, enabling multimodal analytics via SQL and Python.

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Setting Up and Running GraphRAG with Neo4j

Analytics Vidhya

Knowledge graphs provide essential contextual data, enabling LLMs to deliver reliable answers and act as trusted agents in complex tasks. Unlike conventional […] The post Setting Up and Running GraphRAG with Neo4j appeared first on Analytics Vidhya.

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Making the Case for Data Enablement (Rather Than Data Governance)

TDAN

There is a movement in the business and academic worlds to consider relabeling the name of the long-time data discipline of “Data Governance” to “Data Enablement”. Usually, when someone tells me something like this, my first response is to chuckle and nod my head.

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How to Use Mistral OCR for Your Next RAG Model

Analytics Vidhya

Retrieval-Augmented Generation (RAG) systems have revolutionized how we access and utilize vast amounts of data, enabling AI to deliver more informed and context-aware responses.

Modeling 140
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A CIO’s framework for measuring engineering productivity

CIO Business Intelligence

The issue is quantitative data may not detect a problem that people in the midst of the process can clearly see. Survey responses can also add critical explanatory context to the quantitative data, enabling leadership to clearly see the root cause of an issue, he said.

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What is data architecture? A framework to manage data

CIO Business Intelligence

Data architectures should integrate with legacy applications using standard API interfaces. They should also be optimized to share data across systems, geographies, and organizations. Real-time data enablement. Be decoupled and extensible.