Why does interoperable, tool-agnostic infrastructure matter for an annotation platform?
No single annotation tool handles every data type well — audio, video, text, and structured metadata all benefit from different interfaces. Building on generic APIs and data models instead of one fixed tool lets engineers swap or combine tools per task without rewriting the surrounding pipeline every time a new modality or use case shows up.
Answered in
Why Your ML Team's Real Bottleneck Is Annotation, Not Model ChoiceIsolated annotation tooling doesn't scale. A shared platform with tiered human review plus LLM-assisted labeling does — here's the architecture.
Read the full analysisOther questions this article answers
- Why does annotation become a bottleneck as an ML team grows?
- Why use a tiered human review structure instead of one flat pool of annotators?
- How do you decide which annotation cases an LLM can label versus which need a human?
- What's the actual measurable benefit of centralizing annotation into a shared platform?
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