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IBM Watson OpenScale Methodology
Course Description
Overview
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.
Objectives
• 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
Audience
Analysts, Developers, Data Scientists and others who need to monitor machine learning jobs
Prerequisites
• Basic knowledge of cloud platforms, for example IBM Cloud • Basic understanding of machine learning models, and how they are used
Topics
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
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Self-Paced Training Info
Learn at your own pace with anytime, anywhere training
- Same in-demand topics as instructor-led public and private classes.
- Standalone learning or supplemental reinforcement.
- e-Learning content varies by course and technology.
- View the Self-Paced version of this outline and what is included in the SPVC course.
- Learn more about e-Learning
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Self-Paced Training Terms & Conditions
IBM Web Based Training courses are sold on a per-user basis. WBT courses provide a training advantage for you and your teams, helping you get up to speed quickly. Take the courses you need, at your convenience and at your own pace.
You can start the course at any time within 12 months of enrolling for the course. After you register/start the course, you have 30 days to complete your course. Within this 30 days, the self-paced format gives you the opportunity to complete the course at your convenience, at any location, and at your own pace. The course is available 24 hours a day.
System Requirements
To participate in this course, the student workstation must meet the following hardware requirements:- Minimum of 256 MB of memory
- Windows Vista or Windows 7-10 (32-bit or 64-bit edition)
- Internet Explorer 6 or higher or Firefox ESR
- 128-bit encryption
- Citrix Receiver for connection to the IBM Remote Lab Platform
- Java
- Access to Internet with at least 128 kbps down and 128 kbps up
- Other platforms/combinations may work but are not officially supported by IBM.
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