Remove AI Remove Data Lakes Remove Data Quality
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Data Integrity for AI: What’s Old is New Again

Precisely

Artificial Intelligence (AI) is all the rage, and rightly so. By now most of us have experienced how Gen AI and the LLMs (large language models) that fuel it are primed to transform the way we create, research, collaborate, engage, and much more. Can AIs responses be trusted? A data lake! Can it do it without bias?

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Governing the ML lifecycle at scale, Part 3: Setting up data governance at scale

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For example, in the bank marketing use case, the management account would be responsible for setting up the organizational structure for the bank’s data and analytics teams, provisioning separate accounts for data governance, data lakes, and data science teams, and maintaining compliance with relevant financial regulations.

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Build a domain‐aware data preprocessing pipeline: A multi‐agent collaboration approach

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Traditional data preprocessing methods, though functional, might have limitations in accuracy and consistency. This might affect metadata extraction completeness, workflow velocity, and the extent of data utilization for AI-driven insights (such as fraud detection or risk analysis).

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Data Version Control for Data Lakes: Handling the Changes in Large Scale

ODSC - Open Data Science

In the ever-evolving world of big data, managing vast amounts of information efficiently has become a critical challenge for businesses across the globe. As data lakes gain prominence as a preferred solution for storing and processing enormous datasets, the need for effective data version control mechanisms becomes increasingly evident.

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Architect a mature generative AI foundation on AWS

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Generative AI applications seem simpleinvoke a foundation model (FM) with the right context to generate a response. Many organizations have siloed generative AI initiatives, with development managed independently by various departments and lines of businesses (LOBs). This approach facilitates centralized governance and operations.

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A Bridge Between Data Lakes and Data Warehouses

Dataversity

It has been ten years since Pentaho Chief Technology Officer James Dixon coined the term “data lake.” While data warehouse (DWH) systems have had longer existence and recognition, the data industry has embraced the more […]. The post A Bridge Between Data Lakes and Data Warehouses appeared first on DATAVERSITY.

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How AWS sales uses Amazon Q Business for customer engagement

AWS Machine Learning Blog

Earlier this year, we published the first in a series of posts about how AWS is transforming our seller and customer journeys using generative AI. Using our AI assistant built on Amazon Q, team members are saving hours of time each week. This time adds up individually, but also collectively at the team and organizational level.

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