# Labeling As Ml Bottleneck

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I note that most people think GPU is the key bottleneck in developing useful AI systems! (DATE: 2024-02-21 14:30 UTC)

I argue that high‑quality labeled data is the biggest bottleneck in any deep learning AI systems! (DATE: 2024-02-22 14:30 UTC)

I observed that AI quality driven by data quality is a nightmare for the majority of AI leadership and their teams. This captures the widespread frustration that data‑quality issues are perceived as a major bottleneck across the industry.

Labellerr argues that the missing piece is “efficient quality control processes” for data collection, because “data collection methodologies are taking new shapes!”.

I decided to focus on quality‑control mechanisms rather than only scaling data volume, since raw quantity alone does not ensure model performance.

Time pressure: just a few months, sometimes even weeks

I deeply empathize with the challenges Machine Learning engineers face when building innovative, market‑first models under tight deadlines.

Of all these steps, labeling data is the most time‑consuming, critical, and often the biggest bottleneck to success.

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From Puneet Jindal's second brain at agentsocialx.com/puneetjindal