Towards Data Science

Enterprise Document Intelligence [Vol.1 #5quater] - The other parsers read the words on a page. A vision model also reads the pictures The post Vision LLMs are PDF Parsers Too: Reading Charts and Diagrams for RAG appeared first on Towards Data Science .

aimachine-learning

A systems-level deep dive into the hidden microarchitectural costs of Kubernetes GPU time-slicing, and what it actually costs to co-locate Agentic AI workloads. The post GPU Time-Slicing for Concurrent LLM Agents on Kubernetes appeared first on Towards Data Science .

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Increasing context size in RAG systems doesn’t improve accuracy for aggregation tasks—it makes errors harder to detect. In this article, I benchmark retrieval-based pipelines against a deterministic full-scan engine across 100,000 rows and show why computation queries must be routed away from RAG entirely. The post Larger Context Windows Don’t Fix RAG — So I Built a System That Does appeared firs…

aimachine-learning

Enterprise Document Intelligence [Vol.1 #5ter] - Table cells, OCR, captions, headings: cloud-grade structure, running on your own machine. No key, no per-page bill, nothing leaves the building The post Parse PDFs for RAG Locally with Docling: Rich Tables, No Cloud Upload appeared first on Towards Data Science .

Enterprise Document Intelligence [Vol.1 #5bis] - The same relational tables. Native table cells. OCR for scanned pages and images. Captions and headings without regex. The post When PyMuPDF Can’t See the Table: Parse PDFs for RAG with Azure Layout appeared first on Towards Data Science .

algorithmscomputer-scienceprogramming-languages

I tried to make my ETL pipeline production-ready. Three things broke. Each one taught me something scripting alone never could. The post I Thought Data Engineering Was Just Writing Scripts. I Was Wrong. appeared first on Towards Data Science .

Mahdi Karabiben
3d ago

The true bottleneck was never the analysis. The post BI Is Dead, Long Live BI appeared first on Towards Data Science .

Enterprise Document Intelligence [Vol.1 #5B] - One PDF in, a relational set of DataFrames out: lines, pages, TOC, images, cross-references, captions, spans, and a parsing summary The post Stop Returning Flat Text from a PDF: The Relational Shape RAG Needs appeared first on Towards Data Science .

Take the next step to building real workflows with Spark on your laptop The post PySpark for Beginners: Beyond the Basics appeared first on Towards Data Science .

Improve coding agent productiveness with refactored code The post How to Refactor Code with Claude Code appeared first on Towards Data Science .

computer-scienceprogramming-languages

A structured methodology for comparing candidate models, testing stability, and selecting a robust final score The post How to Train a Scoring Model in the Age of Artificial Intelligence appeared first on Towards Data Science .

aimachine-learning

Enterprise Document Intelligence [Vol.1 #5A] - Document signals (metadata, native TOC, source software) and page-level content (text vs scans, tables, images, columns, page profile) The post Beyond extract_text: The Two Layers of a PDF That Drive RAG Quality appeared first on Towards Data Science .

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