AI QA Specialist Course
The course is divided into thematic weeks, each with three sessions, balancing theory and hands-on practice. Below is a detailed breakdown, including content, hands-on activities, and expectations for each session.
Week 1: From Theory to Data
This week, we’ll demystify what an AI model is and immediately jump into your first hands-on data investigation.
- Session 01: AI Fundamentals & The QA Mindset
We’ll cover the basics: What is an AI model? How is it different from normal software? We’ll walk through the AI life cycle (Data -> Model -> Deploy) and pinpoint exactly where QA adds the most value. We’ll also discuss the unique challenges, like testing a “black box.
- Session 02: Introduction to Data Profiling
We’ll introduce the “garbage in, garbage out” principle. You’ll learn what data profiling is and why it’s the first step in any AI testing project. We’ll cover common data issues like missing values, outliers, and incorrect data types.
Week 2: Hands-On Data Validation
This week is all about writing simple tests to ensure the quality and integrity of the data being used to train the model.
- Session 03: Finding and Quantifying Data Issues
We’ll go deeper into specific data problems. We’ll cover the impact of bad data labels(e.g., a picture of a cat labeled as a dog) and how to measure data quality with quantifiable checks.
- Session 04: Writing Your First Data Tests
We’ll introduce the concept of an automated data validation pipeline. You’ll learn how to write formal “unit tests” for your data to ensure it always meets quality standards before it’s used for training.
Week 3: Measuring Model Performance
Now that we trust our data, we can test the model. This week, you’ll learn the essential metrics used to decide if a model is good enough to release.
- Session 05: Understanding the Confusion Matrix
We’ll introduce classification models (e.g., spam or not spam?). You’ll learn how to read a Confusion Matrix—the most important tool for understanding a model’s errors. We’ll define Accuracy, Precision, and Recall in simple terms.
- Session 06: Calculating Metrics with Code
You’ll learn how to move from manual calculations to automated reporting using Python’s most popular machine learning library, Scikit-learn.
Week 4: Testing for Behavior and Robustness
A model can have good metrics but still behave strangely. This week, we’ll write tests for “common sense” behavior and see how the model handles stress.
- Session 07: Behavioral Testing
We’ll cover Behavioral Testing, which is like unit testing for AI. We’ll discuss how to test for basic capabilities, invariance (e.g., changing a name shouldn’t change a loan decision), and directional expectations.
- Session 08: Robustness and Stress Testing
We’ll discuss why models often fail in the real world: messy, unexpected data. You’ll learn about robustness testing and how to create “corrupted” data to see how the model copes.
Week 5: Security and Final Project
This week focuses on the crucial area of Responsible AI. You’ll learn how to find unfair bias in a model and peek inside the “black box.”
- Session 09: Auditing for AI Bias
We’ll explore how AI models can accidentally discriminate against certain groups. We’ll define AI bias and discuss key fairness metrics like Demographic Parity.
- Session 10: Peeking Inside the Black Box (Explainable AI)
We’ll introduce Explainable AI (XAI) and why it’s critical for building trust. We’ll discuss tools like LIME and SHAP that can explain why a model made a specific decision.
Week 6: Making It Work Long-Term & Getting Executive Approval
In our final week, we’ll touch on security and then you’ll apply everything you’ve learned in a final capstone project.
- Session 11: Introduction to AI Security Testing
We’ll cover the basics of AI security. You’ll learn about adversarial attacks, where tiny, invisible changes to an input can trick a model into making a huge mistake.
- Session 12: Capstone: Your First AI QA Sign-Off
We’ll review the key stages of an AI QA process and introduce the final project.