Trustworthy AI for an Enterprise with IBM Watson Studio (W7125G-WBT)

Overview

Achieving trust in AI at an enterprise-grade level has always been challenging. This course will give you an overview on the concepts of various aspects that build trust in machine learning models and how 'IBM Watson Studio' can help you enable trustworthy AI at an enterprise-grade level. Learners will be provided with an overview of different aspects of trustworthy AI and will also get to  experience the working of  IBM Watson Studio which enables customers to build, deploy, and manage trustworthy AI.

 

IBM Customers and Sellers: If you are interested in investing in your training, please consider the following Individual or Enterprise Subscriptions:

  • IBM Learning for Data and AI Individual Subscription (SUBR022G)
  • IBM Learning for Data and AI Enterprise Subscription (SUBR004G)
  • IBM Learning Individual Subscription with Red Hat Learning Services (SUBR023G)

 

Audience

Anyone with an interest in Trustworthy AI concept and technology having the prerequisite knowledge required

Prerequisites

Some basic understanding of AI Lifecycle/ workflow will be helpful

Objective

1. Recognize the need for Trustworthy AI and various aspects that help contribute to it 
2. Identify the benefits of an enterprise grade solution over open source for implementing Trusted AI 
3. Recognize that IBM Watson Studio is an enterprise grade solution for building-running and managing trusted AI 
4. Differentiate and define various concepts like bias, explainability, drift as used when assessing the performance of machine learning models 
5. Interpret the capabilities of IBM Watson Studio in Managing AI models and infusing trust into them

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Course Outline

Trustworthy AI for an Enterprise

  • Recognize the need for Trustworthy AI and various aspects that help contribute to it 
  • Identify the benefits of an enterprise grade solution over open source for implementing Trusted AI 
  • Recognize that IBM Watson Studio is an enterprise grade solution for building-running and managing trusted AI 
  • Differentiate and define various concepts like bias, explainability, drift as used when assessing the performance of machine learning models 
  • Interpret the capabilities of IBM Watson Studio in Managing AI models and infusing trust into them