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Identifying Prediction Processing Issues and Error Analysis
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Classroom Contents
Instrumenting Weights & Biases for PII Data Detection - ML Pipeline Tutorial
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- 1 Introduction to the Session: Overview of topics covered,
- 2 General Approach to Machine Learning Problems
- 3 Explanation of the Kaggle Competition
- 4 Importance of Evaluation Metric
- 5 Overview of Weights and Biases Platform
- 6 Proper Validation Approach
- 7 Approach to Cross-Validation
- 8 Data Visualization and Analysis
- 9 Introduction to Best Experiment Setup
- 10 Discussion on Scroll Price Competition
- 11 Review of Training Script Progress
- 12 Monitoring Training Metrics
- 13 Overview of Logged Evaluation Metrics
- 14 Initial Setup and Dashboard Configuration
- 15 Sharing Code and Future Availability
- 16 - Explaining Dashboard Views and Metrics Interpretation
- 17 Analyzing Model Performance and Error Identification
- 18 - Understanding Token Classification and Model Prediction Process
- 19 Identifying Prediction Processing Issues and Error Analysis
- 20 Explanation of Code for Token Classification and Testing Techniques
- 21 Overview of Experiment Tracking, Data Set Versioning, and Reproducibility
- 22 Q&A
- 23 Outro & Resources to follow
