Machine Learning Explainability & Bias Detection with Watson OpenScale

Learn how to leverage Watson OpenScale for machine learning explainability, debiasing, and drift detection in this comprehensive 23-minute tutorial video. Discover the process of setting up Watson OpenScale, viewing model performance metrics, debiasing machine learning predictions, and explaining and interpreting model predictions. Follow along as the instructor guides you through evaluating model performance, mitigating bias, conducting what-if scenario modeling, tracking model quality, and assessing model and data drift. Gain valuable insights into maintaining and improving deployed machine learning models, ensuring their performance, fairness, and interpretability.

- Start
- Explainer
- How it Works
- Setup Watson OpenScale
- Evaluating Model Performance
- Mitigating and Detecting Bias in ML Models
- Explaining and Interpreting Predictions
- What-If Scenario Modelling using OpenScale
- Tracking Model Quality
- Evaluating Model and Data Drift
- Wrap Up