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Managing ModelOps with IBM Cloud Pak for Data V4.8

This IBM Self-Paced Virtual Class (SPVC) includes: - PDF course guide available to attendee during and after course - Lab environment where students can work through demonstrations and exercises at...

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$650 USD
Course Code 6XS936G-SPVC
Duration 7 hours
Available Formats Self Paced

This IBM Self-Paced Virtual Class (SPVC) includes:
- PDF course guide available to attendee during and after course
- Lab environment where students can work through demonstrations and exercises at their own pace

This learning offering tells a comprehensive story of Cloud Pak for Data, and how you can extend the functions with services and integrations. You explore some of the services, and see how they enable effective collaboration across an organization. In this course, you use IBM Knowledge Catalog, Watson Query, and Watson Studio (including Data Refinery and AutoAI). You also examine some of the industry accelerators that are available on the platform.

Skills Gained

After completing this course, you should be able to:

  • Describe the Cloud Pak for Data implementation stack
  • Summarize the Cloud Pak for Data workflow that implements the ModelOps process
  • Construct a simple predictive model that reflects a typical Data Fabric solution
  • Examine industry accelerators that promote trustworthy AI
  • Select services that align to the goals of data-driven organizations

Who Can Benefit

Architect, Consultant, Data Specialist, Program Management

Prerequisites

Before you start this course, you should be able to complete the following tasks:

  • Explain the purpose of Cloud Pak for Data and the value it brings to the business
  • Describe the architecture of Cloud Pak for Data
  • Differentiate between Cloud Pak for Data and Red Hat OpenShift Container Platform
  • Define the AI Ladder and its associated roles and services

You can review these skills in the Solution Architect - Associate learning path.

Course Details

Course Outline

  • Introduction
  • Explore the Cloud Pak for Data environment
  • Create a project for analyzing data
  • Collect the data
  • Govern the data
  • Prepare the data
  • Analyze the data
  • Monitor the model
  • Consider other scenarios
  • Review and evaluation