Instrumenting Weights & Biases for PII Data Detection - ML Pipeline Tutorial
Discover how to instrument Weights & Biases (W&B) in machine learning pipelines through a practical demonstration using a PII data detection use case. Learn to integrate W&B for experiment tracking, data analysis, and error assessment, applying these skills to a live Kaggle competition. Master the use of W&B Tables for data visualization and error analysis, and explore dataset version control and model checkpoint management with W&B Artifacts. Gain insights into proper validation approaches, cross-validation techniques, and best practices for experiment setup. Delve into token classification, model prediction processes, and effective error identification strategies. Explore dashboard configuration, metrics interpretation, and techniques for ensuring reproducibility in machine learning projects.