Главная
Study mode:
on
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
Description:
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.

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

Weights & Biases
Add to list
#Computer Science #Machine Learning #Data Science #Data Visualization #Model Evaluation #Information Security (InfoSec) #Cybersecurity #Data Privacy #Experiment Tracking
1 of 23

Introduction to the Session: Overview of topics covered,