LLM Agents Underscore One Truth: Data Is The Real Differentiator.

Author(s): Houssem Ben Braiek

Originally published on Towards AI.

We don’t have better algorithms; we just have more data. — Peter Norvig, The Unreasonable Effectiveness of Data.

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Edited Photo by Taylor Vick on Unsplash

In ML engineering, data quality isn’t just critical — it’s foundational.

Since 2011, Peter Norvig’s words underscore the power of a data-centric approach in machine learning. Yet, this perspective often gets sidelined and there was never a consensus in the ML community about it.

Why? Because of how ML practitioners were initially trained.

ML engineers and data scientists, including myself, are trained with a model-centric focus and practice using research-grade datasets. These datasets are rich in documentation, including open-source scripts, and were built with the intent to test ML algorithms. Naturally, our priority was algorithm experimentation, understanding intricate behaviors, and advancing the state-of-the-art.

As a result of this, the ML community and ecosystem we have now were built and ML technology has been democratized.

That early obsession with algorithms was vital.

But when it comes to real-world ML systems, data quality becomes the make-or-break factor. The data must accurately reflect the problem; otherwise, even the most finely-tuned models will fail to deliver in production.

Using biased or low-quality data? — Your model is essentially solving the wrong problem.

The result? — A solution that performs poorly when deployed.

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Published via Towards AI