Training Data

Training data is the collection of examples a model fits its parameters to, and model quality is bounded by it far more than by architecture. Large, fresh, diverse, well-labeled data produces models that generalize to the real world; stale, narrow, or biased data produces models that look good in evaluation and fail on live inputs. For most teams, sourcing good data is the hard part of the pipeline, not choosing the model.

Two failure modes recur. Leakage, where information that would not be available at prediction time sneaks into training, produces a model that scores brilliantly and collapses in production. And staleness, where the training distribution drifts away from the world the model now sees, degrades performance quietly over time. Both are data problems, which is why a continuously refreshed, well-documented dataset is worth more to a training team than a one-time dump.