On 2024‑04‑25 I emphasized that investing in high‑quality labeled data early in the ML pipeline avoids “countless epochs” of training, cuts cloud compute spend, and reduces engineering friction. I urged teams to treat data quality as a gating factor before committing compute resources to fine‑tuning or large‑scale training.
We almost lost a $50k contract because we tried to save $2,000 on data labeling.
The low‑cost vendor promised 99% accuracy.
Labelers had marked "Shadows" as "Obstacles" because the instructions were vague and there was no feedback loop.
The model failure was discovered 2 weeks before deployment.
Labellerr AI now bakes "Human‑in‑the‑loop" into everything we do.
Impact: significant waste in compute resources and costly AI talent hours from one day of bad data.
80% of AI development cost isn’t in the model. It’s in the data.
Our Mission: To democratise access to data by automating up to 90% of the preparation workflow delivering 99.5% accurate data at 10x the speed with domain experts in the loop.
Our Vision: be the foundational data engine for all AI‑lead autonomous systems world.