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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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What is data architecture? A framework to manage data

CIO Business Intelligence

Data architecture definition Data architecture describes the structure of an organizations logical and physical data assets, and data management resources, according to The Open Group Architecture Framework (TOGAF). An organizations data architecture is the purview of data architects. Curate the data.

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The Power of Graph Databases, Linked Data, and Graph Algorithms

Rocket-Powered Data Science

And this: perhaps the most powerful node in a graph model for real-world use cases might be “context”. How does one express “context” in a data model? Hence, the graph model can be applied productively and effectively in numerous network analysis use cases. Ahh, that’s the topic for another article.

Metadata 250
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Rising Tide Rents and Robber Baron Rents

O'Reilly on Data

Perhaps a more direct way to say this in the context of economic value creation is that companies such as Amazon and Google and Facebook had developed a set of remarkable advances in networked and data-enabled market coordination. But over time, something went very wrong. These companies did continue to innovate.

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DataOps For Business Analytics Teams

DataKitchen

Business analytic teams have ongoing deliverables – a dashboard, a PowerPoint, or a model that they refresh and renew. There’s a recent trend toward people creating data lake or data warehouse patterns and calling it data enablement or a data hub. The work product could be a chart, graph, model or dashboard.

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Eight Top DataOps Trends for 2022

DataKitchen

In 2022, data organizations will institute robust automated processes around their AI systems to make them more accountable to stakeholders. Model developers will test for AI bias as part of their pre-deployment testing. Continuous testing, monitoring and observability will prevent biased models from deploying or continuing to operate.

Testing 245
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How AI-powered agriculture helps farmers grapple with climate change and food security

CIO Business Intelligence

The company’s mission is to provide farmers with real-time insights derived from plant data, enabling them to optimize water usage, improve crop yields, and adapt to changing climatic conditions. This system uses large language models (LLMs) to combine a vast library of agricultural data with expert knowledge.

IoT 126