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IBM watsonx.ai: Preparing Data for Prompt Tuning with IBM Data Refinery
Course Description
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.
Objectives
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
Audience
IBM watsonx.ai: Preparing Data for Prompt Tuning with IBM Data Refinery is a course in the learning path for professional data scientists that are working with IBM watsonx.ai.
Prerequisites
None
Topics
- Introduction
- Generate synthetic data
- Profile and visualize data
- Prepare, analyze, and transform data
- Manage Data Refinery flows
- Prompt Tuning
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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Self-Paced Training Terms & Conditions
This is a Self-Paced virtual class; it is intended for students who do not need the support of a classroom instructor.
If you feel you would better benefit from having access to a Subject Matter Expert, please enroll in the Instructor-Led version instead. Minimal technical support is provided to address issues with accessing the platform or problems within the lab environment.
Before you enroll, review the system requirements to ensure that your system meets the minimum requirements for this course. AFTER YOU ARE ENROLLED IN THIS COURSE, YOU WILL NOT BE ABLE TO CANCEL YOUR ENROLLMENT. You are billed for the course when you submit the enrollment form. Self-Paced Virtual Classes are non-refundable. Once you purchase a Self-Paced Virtual Class, you will be charged the full price.
After you receive payment confirmation from LearnQuest, you will be sent further access instructions and time limits for your course from IBM.
IMPORTANT!!! If your course provides access to a hands-on lab (Virtual Lab Environment), you will have a specific number of days (varies course by course) on the remote lab platform to complete your hands-on labs. Do not start your lab until you are ready to use your lab time effectively. Time allotted in the virtual lab environment will be indicated once you log into your course. The self-paced format gives you the opportunity to complete the course at your convenience, at any location, and at your own pace. The course is available 24 hours a day.
If the course requires a remote lab system, the lab system access is allocated on a first-come, first-served basis. When you are not using the elab system, ensure that you suspend your elab to maximize your hours available to use the elab system. Note: This does not add additional days to your Lab Environment time frame.
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Click the Skytap Connectivity Documentation button to read about the hardware, software and internet connection requirements.
Exam Terms & Conditions
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