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.
Virtual Learning
This interactive training can be taken from any location, your office or home and is delivered by a trainer. This training does not have any delegates in the class with the instructor, since all delegates are virtually connected. Virtual delegates do not travel to this course, Global Knowledge will send you all the information needed before the start of the course and you can test the logins.
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
Professional data scientists that are working with IBM watsonx.ai
