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Schema Evolution in Data Lakes

KDnuggets

Whereas a data warehouse will need rigid data modeling and definitions, a data lake can store different types and shapes of data. In a data lake, the schema of the data can be inferred when it’s read, providing the aforementioned flexibility. However, this flexibility is a double-edged sword.

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What Every Business Leader Needs to Know About Data Modeling

Dataversity

But decisions made without proper data foundations, such as well-constructed and updated data models, can lead to potentially disastrous results. For example, the Imperial College London epidemiology data model was used by the U.K. Government in 2020 […].

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Compute-efficient Way to Scale LLM — Journey around data, model, and compute

Towards AI

Photo by Matt Foxx on Unsplash The below analysis aims to look at the problem of how different parameters shape the performance of AI models. Before going deeper, few things to keep in mind: Paper considers compute as the most precious resource and models the equations around compute.

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Master Data Annotation in LLMs: A Key to Smarter and Powerful AI!

Data Science Dojo

A critical component in the success of LLMs is data annotation, a process that ensures the data fed into these models is accurate, relevant, and meaningful. billion in 2020 to $4.1 This indicates the increased demand for high-quality annotated data sources to ensure LLMs generate accurate and relevant results.

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Streamline RAG applications with intelligent metadata filtering using Amazon Bedrock

Flipboard

By combining the capabilities of LLM function calling and Pydantic data models, you can dynamically extract metadata from user queries. In this post, we explore an innovative approach that uses LLMs on Amazon Bedrock to intelligently extract metadata filters from natural language queries.

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On the implementation of digital tools

Dataconomy

For some of the world’s most valuable companies, data forms the core of their business model. The scale of data production and transmission has grown exponentially. However, raw data alone doesn’t equate to actionable insights. In one pivotal project, we faced this challenge head-on.

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Remove the Barriers from AI Adoption

DataRobot

In a 2020 Capgemini Research study, only a meager 13% of businesses had successfully deployed use cases in production and continued to scale the use cases throughout multiple business teams. In 2019, Beacon Street Services needed new data models to enable its marketing team to run more targeted and effective campaigns.