IBM Watson OpenScale Methodology - eLearning (W7069G-WBT)

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.

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

Objective

• 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

Details anzeigen

Course Outline

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