The Certified AI Testing is a GSDC (Global Skill Development Council) certification for QA professionals who need to test machine learning and AI systems no coding or data science background required. This self-paced course takes you through the full ML testing lifecycle: understanding how models learn, validating them for underfitting and overfitting, learning what to ask on any AI project, running 22+ functional and API tests on outputs like ChatGPT's, checking for bias and fairness before release, and monitoring for drift once a model is live. The Introduction and Module 1 are free, so you can judge the teaching before paying anything.
AI testing is the practice of verifying that machine learning models and AI systems behave correctly, fairly and reliably at every stage of their lifecycle, and it requires techniques that traditional software testing does not cover. QA teams in 2026 are being asked to do three things most testers have never been trained for.
An ML model isn't finished when the code compiles; it's finished when it's trained enough to predict correctly on data it hasn't seen. You learn to spot underfitting and overfitting from validation results and tell developers when to add data, simplify the model, or stop training.
A model can pass every functional test and still fail unfairly for one group of users. You learn responsible-AI testing fairness, transparency, and privacy checks as a distinct discipline, not an afterthought bolted onto functional testing.
A traditional app that passed QA stays passed. An ML model degrades as real-world data shifts. You learn post-deployment testing for latency, load and drift, and how to monitor performance in production and feed defects back to the team.
Learners in the Certified AI Testing Professional course complete four practical deliverables that mirror what QA produces on a real ML project: a lifecycle test plan, a model validation report, a model API test collection, and a release-and-monitoring checklist. Each one is something you can show a hiring manager or reuse on your own project.
A stage-by-stage plan mapping QA responsibilities from data preparation through production: what to test in model engineering, what functional testing happens at evaluation, and what continues after deployment. This is the document most teams starting AI projects don't have.
Using the house-price prediction model from the course, you assess prediction-vs-actual results, identify whether the model is underfit, overfit or a good fit, and write the recommendation you'd hand to developers: add data, add features, reduce complexity, or stop training.
A Postman collection that calls a live model endpoint (the course uses the ChatGPT API), validates the responses, and can be re-run as a regression check. You learn how to script assertions for outputs that vary between runs.
Track your mastery seamlessly as you progress through each stage.
Review the key certification exam information before you begin your preparation.
Program Lead, NovelVista Learning Solutions
Vikas Sharma has 23 years of experience in IT service delivery and quality, is a Six Sigma Green Belt, AI testing and is an accredited trainer in DevOps, ITIL 4 and Agile Scrum. He has trained more than 11,000 professionals and oversees the structure, assessments and learner support for the Certified AI Testing Professional program at NovelVista, including its coverage of model validation, responsible AI testing, and production monitoring. His focus is that every module produces a skill you can use at work, not just a topic you have covered.
The AI testing course includes an AI tutor inside each module, a human learning support team, and lifetime access to a private alumni community. Self-paced does not mean alone.
“I wanted to move beyond traditional testing and develop practical AI testing skills. This AI Testing Course helped me understand how AI is changing testing and quality assurance, and how to apply these concepts to real project scenarios.”
“I gained hands-on knowledge, improved my confidence, and feel better prepared to take on AI-focused testing responsibilities within my team.”
See how learners apply these skills in real roles and real environments.
“I come from a traditional QA background, and this course helped me understand how testing changes when working with AI and ML systems. The sections on model validation, API testing, and AI outputs were especially useful. It gave me a clearer understanding of where AI testing fits into a QA role.”
“The course gave me a structured way to approach testing AI-based applications. I particularly liked the focus on validating model behavior, testing APIs, and considering issues such as bias and fairness. The practical orientation made the concepts easier to connect with real QA work.”
“I was looking for a structured way to build my understanding of AI testing without starting from a data science perspective. The course covers the AI testing lifecycle clearly and helped me understand areas such as model performance, functional testing, and production monitoring.”
Enroll and get access to career-accelerating resources.
A comprehensive PDF of technical questions commonly asked by leading companies. ($99 Value)
Templates and strategies to highlight practical project and lab experience. ($149 Value)
Lifetime access to networking, peer learning, and career opportunities. (Priceless Value)
Choose the plan that fits your professional goals.
The Certified AI Testing Professional course costs ($199) as a one-time payment, and Module 1 is free with no card required.
The complete professional certification path for serious learners.
Upskill a group with coordinated learning, administrative visibility and team enrollment support.
Start with the essentials, then upgrade when you are ready for the complete professional learning journey.
If the premium learning experience is not right for you, request a refund within 30 days according to the enrollment terms.
Everything you need to know before enrolling.
