Instrumenting Weights & Biases for PII Data Detection - ML Pipeline Tutorial

This session demonstrates instrumenting a machine learning workflow with W&B using a PII detection competition. It covers validation, experiment tracking, data visualization and error analysis, dataset versioning, and reproducibility.

Syllabus

Introduction to the Session: Overview of topics covered,
General Approach to Machine Learning Problems
Explanation of the Kaggle Competition
Importance of Evaluation Metric
Overview of Weights and Biases Platform
Proper Validation Approach
Approach to Cross-Validation
Data Visualization and Analysis
Introduction to Best Experiment Setup
Discussion on Scroll Price Competition
Review of Training Script Progress
Monitoring Training Metrics
Overview of Logged Evaluation Metrics
Initial Setup and Dashboard Configuration
Sharing Code and Future Availability
- Explaining Dashboard Views and Metrics Interpretation
Analyzing Model Performance and Error Identification
- Understanding Token Classification and Model Prediction Process
Identifying Prediction Processing Issues and Error Analysis
Explanation of Code for Token Classification and Testing Techniques
Overview of Experiment Tracking, Data Set Versioning, and Reproducibility
Q&A
Outro & Resources to follow