I reflected on 2024‑05‑23 21:00 UTC that speeding up a data annotation pipeline can become a tightrope walk, warning that adding too many human reviewers (too many chefs) can lead to inconsistent QA and bad data.
self.annotation_speed_warning_timestamp = 2024-05-23 21:00 UTC
I decided to avoid simply adding more human reviewers because inconsistent rule interpretation leads to more errors; instead I promote a safety‑net layer (validation, consensus, or automated checks) that catches slip‑ups while keeping the pipeline fast.
Labellerr’s platform can annotate millions of images and audio files in just a few weeks using either manual or automated processes.
I argue that the choice between manual and automated annotation doesn’t matter for AI teams; what matters is increased productivity, affordable training data, and consistently higher accuracy.