Building a Data Annotation Pipeline That Scales

*Notes from the field · Infrastructure*

Key takeaways (my version)

• Throughput without a story loses you money. If you cannot explain what "done" means at 10x volume, you will relabel the same slice three times. - Tooling helps, culture saves you. Dashboards catch drift; humans still have to agree it matters. - Buyers and builders talk past each other. This post sits in the middle on purpose. ---

Why this topic keeps showing up in meetings

The AI industry is still selling magic, but shipping teams live in spreadsheets, Slack threads, and late-night triage. Whether you are a creator trying to monetize careful work or an ML lead begging for clean eval sets, the bottleneck is usually the same: the pipeline outran the spec. ---

What we will actually cover

• Practical habits that survive headcount growth - Where "best practice" advice breaks first - A blunt closing thought on who Harbor is building for --- *Harbor is building infrastructure for the AI training data economy. Learn more at harborml.com.*

Bottom line

Building a Data Annotation Pipeline That Scales *Notes from the field · Infrastructure*