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IBM Cloud Pak for Data (V4.x) Foundations
Course Description
Overview
This learning offering tells a holistic story of Cloud Pak for Data and how you can extend the functions with services and integrations. You will explore some of the services and see how they enable effective collaboration across an organization. In this course, you will use Watson Knowledge Catalog, Data Virtualization, and Watson Studio (including Data Refinery and AutoAI). You will also examine some of the external data sets and industry accelerators that are available on the platform.
Objectives
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
Audience
Anyone who wants to gain foundational knowledge of IBM Cloud Pak for Data
Prerequisites
- Explain the purpose of Cloud Pak for Data and the value it brings to the business
- Describe its basic architectureÂ
- 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
Before you start this course, you should be able to complete the following tasks:
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You can review these skills in the Solution Architect - Associate learning path.
Topics
Create an analytics project
- Summarize the ModelOps processÂ
- Relate a process to a workflowÂ
- Identify the predefined roles in Cloud Pak for DataÂ
- Define analytics projectÂ
- Create an analytics project (data scientist)Â
- Request data (data scientist)
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Add data to the project
- Respond to a data requestÂ
- Evaluate adding data from an integrated database versus data virtualizationÂ
- Differentiate between platform and service level connectionsÂ
- Access an integrated database (data engineer)Â
- Create a catalog (data engineer)Â
- Connect to a data source (data engineer)Â
- Construct a virtualized table from a single data source (data engineer)
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Organize the data
- Describe catalogs and their usesÂ
- Summarize what you can do with the Watson Knowledge Catalog serviceÂ
- List the types of governance artifactsÂ
- Identify how to manage risk and regulatory challengesÂ
- Profile data assets (data steward)Â
- Define a data protection rule (data steward)
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Prepare the data
- List the ways to prepare data for use in projectsÂ
- Describe what you can do with Data Refinery Â
- Prepare data for modeling (data quality analyst)Â
- Validate data (data quality analyst)Â
- Visualize data (data quality analyst)Â
- Develop a Data Refinery flow (data quality analyst)Â
- Create a data set for modeling (data quality analyst)
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Analyze the data and build a model
- Name the steps in the data analysis processÂ
- List the criteria for choosing a modeling tool in analytics projectsÂ
- Summarize the AutoAI requirementsÂ
- Outline the AutoAI processÂ
- Articulate the deployment processÂ
- Describe how to use notebooksÂ
- Build an AutoAI model (data scientist)Â
- Save an AutoAI pipeline model (data scientist)Â
- Deploy a model (data scientist)Â
- Save an experiment as a notebook
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Expand to other scenarios
- Indicate how to monitor modelsÂ
- List the aspects of trustworthy AIÂ
- Identify how to collaborate with external stakeholdersÂ
- Describe how to extend Cloud Pak for Data functions Â
- Define scaling servicesÂ
- Classify servicesÂ
- List the most popular services from each categoryÂ
- Associate Cloud Pak for Data use cases with the services that support themÂ
- Explore solutions (solutions, services, external data sets and industry accelerators)
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Related Courses
-
Installation of IBM Cloud Pak for Data (V4.x)
6XL538G- Duration: 6 Hours
- Delivery Format: Classroom Training, Online Training
- Price: 815.00 USD
-
Enterprise catalog management and data protection with Watson Knowledge Catalog on IBM Cloud Pak for Data 4.0.x
6XL534G- Duration: 6 Hours
- Delivery Format: Classroom Training, Online Training
- Price: 815.00 USD
Self-Paced Training Info
Learn at your own pace with anytime, anywhere training
- Same in-demand topics as instructor-led public and private classes.
- Standalone learning or supplemental reinforcement.
- e-Learning content varies by course and technology.
- View the Self-Paced version of this outline and what is included in the SPVC course.
- Learn more about e-Learning
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