6XL736G  Managing ModelOps with IBM Cloud Pak for Data V4.6

Duration:    7 Hours

Level:          Intermediate

Audience:   Architect, Systems Administrator, Data Scientist

Next Sessions
Start (YYYY-MM-DD) End (YYYY-MM-DD) Language Amount
2024-05-10 2024-05-10 English 1250 EUR 1591
2024-05-24 2024-05-24 English 1250 EUR 1605
2024-06-07 2024-06-07 English 1250 EUR 1619
2024-06-21 2024-06-21 English 1250 EUR 1633
2024-07-05 2024-07-05 English 1250 EUR 1647
2024-07-19 2024-07-19 English 1250 EUR 1661
2024-08-02 2024-08-02 English 1250 EUR 1675
2024-08-16 2024-08-16 English 1250 EUR 1689
2024-08-30 2024-08-30 English 1250 EUR 1703
2024-09-13 2024-09-13 English 1250 EUR 1717
2024-09-27 2024-09-27 English 1250 EUR 1731
2024-10-11 2024-10-11 English 1250 EUR 1745
2024-10-25 2024-10-25 English 1250 EUR 1759
2024-11-08 2024-11-08 English 1250 EUR 1773
2024-11-22 2024-11-22 English 1250 EUR 1787
2024-12-06 2024-12-06 English 1250 EUR 1801
2024-12-20 2024-12-20 English 1250 EUR 1815
Overview

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.

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.

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
  • 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