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#veri kalitesi

5 posts

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blog-bilgisayarli-goru

How to Manage Data Quality, Domain Shift, and Real-World Performance in Vision Systems

High benchmark accuracy in vision systems is not enough to guarantee reliable real-world behavior. A model may perform strongly in controlled evaluation settings yet degrade significantly in production due to camera variation, lighting changes, background diversity, label quality issues, class imbalance, rare scenarios, device differences, seasonal changes, and workflow drift. That is why modern computer vision projects are not only about model architecture. They require strong data quality management, domain shift analysis, slice-based evaluation, error-cost awareness, production monitoring, and continuous improvement loops. This guide explains how to manage data quality, diagnose domain shift, measure real-world performance, and build robust vision systems that remain reliable beyond the lab.

31 min
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blog-dogal-dil-isleme

Data, Morphology, and Evaluation Challenges in Turkish NLP Projects

Turkish NLP projects may look similar to general natural language processing tasks on the surface, but they involve distinct challenges in data, morphology, and evaluation. Agglutinative structure, rich inflection, surface-form explosion, the semantic role of suffixes, spelling variation, colloquial usage, code-switching, domain-specific terminology, and limited high-quality datasets make Turkish NLP much more than a simple “collect more data” problem. In addition, evaluation in Turkish NLP is often misleading when reduced to standard metrics alone, because token-level accuracy, task success, morphological correctness, rare-case performance, and production robustness are not the same thing. This guide explains the major data, morphology, and evaluation challenges in Turkish NLP projects and presents practical solution strategies across classification, NER, retrieval, LLM, and enterprise NLP settings.

30 min