Learning path: Getting started with IBM Cloud Pak for Data

Level Topic Type
100 Introduction to IBM Cloud Pak for Data Article
101 Virtualizing Db2 Warehouse data with data virtualization Tutorial
201 Data visualization with data refinery Tutorial
202 Find, prepare, and understand data with Watson Knowledge Catalog Tutorial
301A Data analysis, model building, and deploying with Watson Machine Learning with notebook Pattern
301B Automate model building with AutoAI Tutorial
301C Build a predictive machine learning model quickly and easily with IBM SPSS Modeler Tutorial
401 Monitoring the model with Watson OpenScale Pattern

This learning path is designed for anyone interested in quickly getting up to speed with using IBM Cloud Pak for Data. This learning path consists of step-by-step tutorials and patterns that explain the process of working with data using IBM Cloud Pak for Data.

To get started, click on a card below, or see the previous table for a complete list of topics covered.

Introduction to IBM Cloud Pak for Data


Learn about:

  • What is IBM Cloud Pak for Data?
  • Terms and Concepts
  • Take a product walkthrough
  • Architecture

Virtualizing Db2 Warehouse data with data virtualization


Learn about:

  • Adding data sets to IBM Cloud Pak for Data
  • Adding a data source to data virtualization
  • Virtualizing the data and create a joined view
  • Assigning virtualized data to a project
  • Adding roles to users and perform admin tasks

Data visualization with data refinery


Learn about:

  • Loading data into the IBM Cloud Pak for Data platform for use with data refinery
  • Transforming a sample data set
  • Using Data Flow steps to keep track of your work
  • Visualizing the data with charts and graphs

Find, prepare, and understand data with Watson Knowledge Catalog


Learn about:

  • Setting up the catalog and data
  • Adding collaborators, control access, and categories
  • Adding data classes, business terms, and policy rules

Data analysis, model building, and deploying with Watson Machine Learning with notebook


Learn about:

  • Using Jupyter Notebooks to analyze data
  • Running Notebooks in IBM Cloud Pak for Data
  • Building, testing and deploying a machine learning model
  • Deploying a selected machine learning model
  • Creating front-end app to interface with the deployed model

Automate model building with AutoAI


Learn about:

  • Handling regression and classification problems without code
  • Using this service for feature engineering, model selection, and hyperparameter tuning
  • Choosing the best model among the piplelines
  • Deploying and using models via IBM Cloud Pak for Data

Build a predictive machine learning model quickly and easily with IBM SPSS Modeler


Learn about:

  • Uploading data to IBM Cloud Pak for Data
  • Creating an SPSS Modeler flow
  • Using the SPSS tool to inspect data and glean insights
  • Modifying and preparing data for AI model creation using SPSS
  • Training a machine learning model with SPSS and evaluate the results

Monitoring the model with Watson OpenScale


Learn about:

  • Setting up Watson OpenScale Data Mart
  • Binding Watson Machine Learning to OpenScale Data Mart
  • Enabling payload logging and performance monitor
  • Scoring German credit model using Machine Learning
  • Using Data Mart to access tables data via subscription


Next: Introduction to IBM Cloud Pak for Data

Scott D’Angelo