Machine Learning Explainability & Bias Detection with Watson OpenScale скачать в хорошем качестве

Machine Learning Explainability & Bias Detection with Watson OpenScale

So you've built a model. It's deployed. Now what? How do you know if it's performing well? How do you keep track of predictions? Better yet, how do you explain them? In this video, you'll learn how to do exactly that using Watson OpenScale. in 20ish minutes I'll walk you through how to leverage Watson OpenScale for machine learning explainability, debiasing and drift detection. In this video you'll learn how to: 1. Setting up Watson OpenScale 2. Viewing Model Performance Metrics like Accuracy, AUC, Precision 3. Debiasing Machine Learning Predictions 4. Explaining and Interpret Machine Learning Model Predictions Links Mentioned IBM Cloud Register: https://cloud.ibm.com/registration Watson OpenScale: https://cloud.ibm.com/catalog/service... Chapters 0:00 - Start 0:27 - Explainer 1:26 - How it Works 2:03 - Setup Watson OpenScale 6:21 - Evaluating Model Performance 12:30 - Mitigating and Detecting Bias in ML Models 14:39 - Explaining and Interpreting Predictions 17:09 - What-If Scenario Modelling using OpenScale 19:23 - Tracking Model Quality 20:19 - Evaluating Model and Data Drift 22:47 - Wrap Up Oh, and don't forget to connect with me! LinkedIn: https://bit.ly/324Epgo Facebook: https://bit.ly/3mB1sZD GitHub: https://bit.ly/3mDJllD Patreon: https://bit.ly/2OCn3UW Join the Discussion on Discord: https://bit.ly/3dQiZsV Happy coding! Nick P.s. Let me know how you go and drop a comment if you need a hand!