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

Anyone who wants to gain foundational knowledge of IBM Cloud Pak for Data

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 its basic architecture 
• State its deployment options
• Differentiate between Cloud Pak for Data and Red Hat OpenShift Container Platform
• Define the AI Ladder and its associated roles and services
• Identify the types of projects and how to collaborate on the platform
• Log in to Cloud Pak for Data and create an analytics project

 

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

Create an analytics project

• Summarize the ModelOps process 
• Relate a process to a workflow 
• Identify the predefined roles in Cloud Pak for Data 
• Define analytics project 
• Create an analytics project (data scientist) 
• Request data (data scientist)

Add data to the project

• Respond to a data request 
• Evaluate adding data from an integrated database versus data virtualization 
• Differentiate between platform and service level connections 
• Access an integrated database (data engineer) 
• Create a catalog (data engineer) 
• Connect to a data source (data engineer) 
• Construct a virtualized table from a single data source (data engineer)

Organize the data

• Describe catalogs and their uses 
• Summarize what you can do with the Watson Knowledge Catalog service 
• List the types of governance artifacts 
• Identify how to manage risk and regulatory challenges 
• Profile data assets (data steward) 
• Define a data protection rule (data steward)

Prepare the data

• List the ways to prepare data for use in projects 
• Describe what you can do with Data Refinery  
• Prepare data for modeling (data quality analyst) 
• Validate data (data quality analyst) 
• Visualize data (data quality analyst) 
• Develop a Data Refinery flow (data quality analyst) 
• Create a data set for modeling (data quality analyst)

Analyze the data and build a model

• Name the steps in the data analysis process 
• List the criteria for choosing a modeling tool in analytics projects 
• Summarize the AutoAI requirements 
• Outline the AutoAI process 
• Articulate the deployment process 
• Describe how to use notebooks 
• Build an AutoAI model (data scientist) 
• Save an AutoAI pipeline model (data scientist) 
• Deploy a model (data scientist) 
• Save an experiment as a notebook

Expand to other scenarios

• Indicate how to monitor models 
• List the aspects of trustworthy AI 
• Identify how to collaborate with external stakeholders 
• Describe how to extend Cloud Pak for Data functions  
• Define scaling services 
• Classify services 
• List the most popular services from each category 
• Associate Cloud Pak for Data use cases with the services that support them 
• Explore solutions (solutions, services, external data sets and industry accelerators)