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In practice, model performance is deeply constrained by the data used during training. Sophisticated models trained on limited or poorly curated datasets rarely outperform simpler models trained on richer and more representative data.

One of the most persistent bottlenecks is not model architecture. It is data preparation.

Video annotation tools are a big part of a larger ecosystem of data labeling tools.

I have been in Data Ops for 8 years, working on projects in every industry…

As we all know by now, a very good model with crappy data, will get you…well…a crappy model performance.

LLMs beneficial to a no-code ETL solution.