IBM Watson OpenScale Methodology

Introduction to IBM Watson OpenScale • Describe the problem that Watson OpenScale solves • Describe models, monitors, workflow • Describe AIF and AIE 360 toolkits • Describe workflow Watson OpenScale architecture • Describe Watson OpenScale architecture on IBM Cloud and on IBM Cloud Pak for Data • Describe how Watson OpenScale works with other cloud services Get started with Watson OpenScale • Provision from catalog • Start working with Watson OpenScale Overview of Watson OpenScale monitors • Identify the different Watson OpenScale monitors • Define how the different monitors are used Explore a use case • Prepare the model for monitoring Build and configure the fairness monitor • Features to monitor • Values that represent a favorable outcome of the model • Reference and monitored groups • Fairness thresholds • Sample size • Insights and explainability Configure the quality monitor • Quality alert threshold • Sample size • Insights and explainability Detect drift and configure the drift monitor • Alert threshold • Sample size • Insights and explainability Configure application monitors • Configure application monitors • Configure KPI metrics in Watson OpenScale • Configure event details • Access and visualize custom metrics

Analysts, Developers, Data Scientists and others who need to monitor machine learning jobs

• Basic knowledge of cloud platforms, for example IBM Cloud  • Basic understanding of machine learning models, and how they are used

• Introduction to IBM Watson OpenScale • Watson OpenScale architecture • Get started with Watson OpenScale • Overview of Watson OpenScale monitors • Explore a use case • Build and configure the fairness monitor • Configure the quality monitor • Detect drift and configure the drift monitor • Configure application monitors