What you'll learn
Understand the current state and expected trends of AI
Design and execute test plans and test cases for AI-based systems
Contribute to the test strategy for an AI-Based system
Understand the challenges associated with testing AI-Based systems, such as their self-learning capabilities, bias, ethics, complexity, transparency
Recognize where testers can best influence quality for AI based systems.
Requirements
Basic knowledge about software testing and AI systems is recommended, but not strictly necessary for this course
Description
A non-technical course on testing AI based systems. Interesting for anyone who is considering using, building, or integrating AI based systems.
This course will teach you the "what's" you need to take into consideration when building, developing, deploying, testing or just using quality AI based systems. We are going to look at
- What is AI and what is ML
- The current state of AI
- What the quality characteristics for AI based systems are
- Challenges related to AI based systems, such as self-learning systems
- Which counter measures you can take to influence the quality
- The machine learning workflow
- Which methods and techniques you can use for testing AI based systems
- Why test automation might not be your first go-to in AI testing.
After you have completed the entire course, you will be able to create test plans, test scripts and scenarios for AI based systems. You can also identify which risks might be applicable for your particular AI project, you can monitor the Machine learning workflow process and measure the quality of data used within your project or organization. Furthermore you will be able to use the test methods and test techniques discussed in the course to actually execute tests for an AI based system.
Who this course is for
Software testers with an interest in testing AI based systems
ML engineers with an interest in quality
Software developers with an interest in quality
Managers, Product owners, Product managers
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