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Data Integrity Trends for 2023

Precisely

Data Volume, Variety, and Velocity Raise the Bar Corporate IT landscapes are larger and more complex than ever. Cloud computing offers some advantages in terms of scalability and elasticity, yet it has also led to higher-than-ever volumes of data. That approach assumes that good data quality will be self-sustaining.

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Simplify access to internal information using Retrieval Augmented Generation and LangChain Agents

AWS Machine Learning Blog

The following risks and limitations are associated with LLM based queries that a RAG approach with Amazon Kendra addresses: Hallucinations and traceability – LLMS are trained on large data sets and generate responses on probabilities. These data points are used to inform a decision based on the company’s internal loan policies.

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Learn the Differences Between ETL and ELT

Pickl AI

By employing ETL, businesses ensure that their data is reliable, accurate, and ready for analysis. This process is essential in environments where data originates from various systems, such as databases , applications, and web services. The key is to ensure that all relevant data is captured for further processing.

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Meet the Final Winners of the U.S. PETs Prize Challenge

DrivenData Labs

Our framework involves three key components: (1) model personalization for capturing data heterogeneity across data silos, (2) local noisy gradient descent for silo-specific, node-level differential privacy in contact graphs, and (3) model mean-regularization to balance privacy-heterogeneity trade-offs and minimize the loss of accuracy.