Why does annotation become a bottleneck as an ML team grows?
Every model needs ground-truth labels for training and evaluation, but if each team builds its own annotation tooling and manages its own reviewers independently, none of that work compounds. Annotation ends up treated as a one-off task per project instead of a shared capability, so the tooling, quality processes, and workforce coordination all get rebuilt from scratch every time a new use case appears.
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 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?
- Why does interoperable, tool-agnostic infrastructure matter for an annotation platform?
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