Should we spend more time fine-tuning our models or cleaning our training data for better results?

All answers to this question.

You've hit the nail on the head. For a long time, the industry was obsessed with the "Model," but we've reached a point of diminishing returns there. In 2024, the biggest gains are coming from high-quality, consistent labeling. If your training data has 10% incorrect labels, no amount of "Tuning" will fix that. We started using "Error Analysis" to find where the model was most confused and then manually reviewed those specific data points. This led to a 15% increase in performance, which was far more than we ever got from switching between XGBoost and LightGBM.

   Answered 2025-06-15 by Kimberly Vance

How much of your data cleaning process is currently automated versus manual "eyeballing" by your junior data scientists?

   Answered 2025-06-16 by Steven Miller

• Automation is key, but it has limits. For a robust workflow, you need a "Human-in-the-Loop" system. We use automated outlier detection to flag suspicious rows, but a human makes the final call on whether it's "bad data" or just a rare, important event. In 2024, we saw that fully automated cleaning often smoothed out the very anomalies we were trying to detect! Finding that balance between programmatic scale and human intuition is where the real magic happens.

     Commented 2025-06-17 by Lawrence Taylor

High-quality data is the only foundation. As Andrew Ng says, "AI is code plus data." If the data is broken, the whole system is broken.

   Answered 2025-06-18 by Posted By:

• Exactly, Douglas. We treat our "Data Pipeline" as a first-class product now, with its own unit tests and version control. It changed everything for us.

     Commented 2025-06-19 by Michael O'Connor