What's the actual measurable benefit of centralizing annotation into a shared platform?
Consolidating annotation into shared tooling and workforce coordination compounds benefits across every future ML use case instead of each one paying full setup cost independently — faster training cycles because labeled data moves through fewer handoffs, better ground-truth quality from structured escalation and agreement scoring, and lower marginal cost for each new project since the tooling and workforce already exist.
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?
- Why does interoperable, tool-agnostic infrastructure matter for an annotation platform?
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