Overview

In this course, you will learn how watsonx.ai can help you put AI to work in your business. You begin by generating synthetic data which mimics enterprise data, then use the data refinery to quickly transform that raw data into consumable, high-quality information that’s ready for analytics through data preparation methods such as data cleansing and data shaping. Finally, you use that transformed data to tune a foundation model for a classification use case. The course has a large practical component with hands-on exercises on generating data, cleansing and shaping data, and prompt tuning, in order to solve several use cases.

Audience

Professional data scientists that are working with IBM watsonx.ai

Prerequisites

Objective

After completing this course, you should be able to: 

- Differentiate between data cleansing and data shaping

- Identify use cases where synthetic data may be used to augment or replace real data

- Compare the differences between data profiling and data visualization

- Understand the categories of operations and functions based on data needs

- Administer Data Refinery flow tasks and jobs

- Examine the differences between prompt tuning and prompt engineering and experiment with foundation models in watsonx.ai

Course Outline

• Introduction
• Generate synthetic data
• Profile and visualize data
• Prepare, analyze, and transform data
• Manage Data Refinery flows
• Prompt Tuning