Data-Centric Machine Learning: A Practical Guide to Improving Model Performance, Labeling, and Drift Management
Shifting to a data-centric approach is one of the most practical ways to improve machine learning outcomes. Rather than chasing marginal gains by swapping model architectures, focusing on the quality, coverage, and labeling of the dataset typically yields faster, more reliable performance improvements. Here’s a clear guide to adopting a data-centric mindset and concrete steps […]