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When is data too clean to be useful for enterprise AI?

CIO Business Intelligence

Good data governance has always involved dealing with errors and inconsistencies in datasets, as well as indexing and classifying that structured data by removing duplicates, correcting typos, standardizing and validating the format and type of data, and augmenting incomplete information or detecting unusual and impossible variations in the data.

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A Comprehensive Guide to Output Parsers

Analytics Vidhya

Output parsers are essential for converting raw, unstructured text from language models (LLMs) into structured formats, such as JSON or Pydantic models, making it easier for downstream tasks. Output Parsers […] The post A Comprehensive Guide to Output Parsers appeared first on Analytics Vidhya.

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Building End-to-End Data Pipelines: From Data Ingestion to Analysis

KDnuggets

Streaming: Use tools like Kafka or event-driven APIs to ingest data continuously. Its key goals are to store data in a format that supports fast querying and scalability and to enable real-time or near-real-time access for decision-making. Key questions: Should you use a data warehouse, a data lake, or a hybrid (lakehouse) approach?

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Essential Skills for the Modern Data Analyst in 2025

DataFloq

Soft Skills and Acceptance of Change In modern times, techniques and data technology application knowledge are imperative in any work environment that deals with structured data. The difference lies in one's interactive, adaptive skills as a data analyst and more.

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Beyond the hype: Do you really need an LLM for your data?

CIO Business Intelligence

While this process is complex and data-intensive, it relies on structured data and established statistical methods. This is where an LLM could become invaluable, providing the ability to analyze this unstructured data and integrate it with the existing structured data models.

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Building TensorFlow Pipelines with Vertex AI

Analytics Vidhya

How can you ensure your machine learning models get the high-quality data they need to thrive? In todays machine learning landscape, handling data well is as important as building strong models. Feeding high-quality, well-structured data into your models can significantly impact performance and training speed.

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AI’s Achilles’ Heel: The Data Quality Dilemma

DataFloq

However, there are additional complexities faced when dealing with the nontraditional data that AI often makes use of. AI Data Has Different Quality Needs When AI makes use of traditional structured data, all the same data cleansing processes and protocols that have been developed over the years can be used as-is.