When does class start/end?
Classes begin promptly at 9:00 am, and typically end at 5:00 pm.
This IBM Web-Based Training (WBT) is Self-Paced and includes: - Instructional content available online for duration of course - Visuals without hands-on lab exercises You will learn how Watson...Read More
This IBM Web-Based Training (WBT) is Self-Paced and includes:
- Instructional content available online for duration of course
- Visuals without hands-on lab exercises
You will learn how Watson OpenScale lets business analysts, data scientists, and developers build monitors for artificial intelligence (AI) models to manage risks. You will understand how to use Watson OpenScale to build monitors for quality, fairness, and drift, and how monitors impact business KPIs. You will also learn how monitoring for unwanted biases and viewing explanations of predictions helps provide business stakeholders confidence in the AI being launched into production. Note: This course contains the same topics as 6X240G IBM Watson OpenScale on IBM Cloud Pak for Data WBT.
- 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
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- 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