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Guides, comparisons and field notes on data annotation for computer vision and audio AI.
Amazon MTurk Is Shutting Down - Here’s How to Prevent Disruption
Amazon MTurk closes Sept 30, 2026. Learn what it means for data labeling and when to use crowdsourcing, your own team, or a professional labeling service.
Top AI & Computer Vision Conferences to Attend in 2026 (Plus a 2027 Sneak Peek 👀)
What are the 10 biggest AI & computer vision conferences in 2026? Read this article to see the 10 we are most looking forward to, and what makes them special.
CVAT vs. Azure Machine Learning Data Labeling: Complete Side-by-Side Comparison (2026)
A direct, engineering-focused comparison of CVAT and Azure ML data labeling features, formats, cloud architectures, and operational workflows.
What is Semantic Segmentation & How Does it Work?
Find out what semantic segmentation is, how it works, and why it’s a critical technique for computer vision applications.
Consensus in Data Labeling: Benefits, Tradeoffs, and Common Mistakes
This article covers what consensus actually does, where it pays off, where it quietly wastes money, and the mistakes that show up again and again in real pipelines, with input from a core CVAT developer on what teams get wrong.
How to Write Annotation Guidelines That Actually Work in Production
Most annotation guidelines fail in production, not because of bad annotators, but because of vague specs. Here’s how to write guidelines that actually hold up at scale.
How to Choose the Right Dataset Format for Your ML Project
Discover how to select the optimal dataset format for your ML project. Evaluate the technical trade-offs of COCO, YOLO, Pascal VOC, and specialized schemas.