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 Watson Knowledge Catalog, Watson Query, and Watson Studio (including Data Refinery and AutoAI). You also examine some of the external data sets and industry accelerators that are available on the platform.

By the end of this course, you will 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 external data sets and industry accelerators that promote trustworthy AI
• Select services that align to the goals of a data-driven organization

Solution Architects, Consultants, Data Specialists, Program Managers, and anyone who wants to explore how IBM Cloud Pak for Data supports the ModelOps process

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.

• 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