I observed that more than 50 % of machine‑learning engineering effort is consumed by checking the quality of labeled datasets, whether it’s images, videos, etc.
We are revolutionizing this process, cutting the effort from days or hours to mere minutes.
I observed that the performance of an AI system is only as good as the quality of its labeled training data, and that quality depends on a rigorous QA process.
- Current QA staffing: 3 people reviewing thousands of annotations by hand.
I consider this approach "not a quality system" and liken it to "a prayer", indicating the need for a more scalable, robust QA methodology. See also data quality beats algorithms.
Decision: Shift focus from sample‑centric optimization to full‑dataset validation (2024‑10‑17)