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100% fundable

AI+ Quality Assurance Practitioner™

If you’re responsible for quality, you need the right tools. AI opens new possibilities to catch errors earlier, steer processes and base decisions on data. This course shows exactly how that works.

Key topics

  • Applying AI methods in quality testing and test planning
  • Pattern recognition and automated analysis of quality data
  • Risk-based quality models and predictive testing approaches
  • Embedding AI into agile workflows – from development to delivery
  • Transparency, compliance requirements and responsibility in AI use

Prerequisite
Knowledge from IT, testing or adjacent quality domains makes it much easier to get started.

Target group
Specialists from QA, development and IT management who want to use AI specifically to improve quality.

AI-driven quality assurance is no longer a future topic – this course gives you the tools to play an active part.
 

course content
  • Core concepts of quality assurance and their connection to AI
  • How AI transforms and enhances QA processes
  • QA metrics and key performance indicators explained
  • Data-driven decisions in quality assurance
  • What defines AI and how it works
  • Machine learning – concepts and application areas
  • Understanding and placing deep learning in context
  • Large language models (LLMs) and their relevance for QA
  • Automating test processes – methods and principles
  • Generating test cases automatically with AI
  • Tools for AI-powered test automation
  • Integrating AI testing into CI/CD pipelines
  • Methods for early error detection
  • Preventive measures in quality assurance
  • Risk-based testing with AI support
  • Case study: reducing error rates with AI
  • How NLP works and where it is applied
  • NLP methods in QA processes
  • LLMs to support quality assurance tasks
  • Case study: NLP in error analysis and resolution
  • Principles and goals of performance testing
  • AI methods for analysing performance data
  • Visualising and interpreting key performance metrics
  • Case study: AI-driven performance testing in cloud environments
  • AI support for exploratory testing
  • Running security tests with AI methods
  • Case study: improving security quality through AI
  • Concept and benefits of continuous testing
  • Making regression testing more efficient with AI
  • Case study: risk-oriented testing in ongoing maintenance
  • Predictive analytics for quality forecasting
  • Identifying and covering edge cases with AI
  • Where AI-driven QA is heading
  • Applying all learned methods in a hands-on QA project

Up to 100% funding for your continuing education

Attractive funding opportunities are available for numerous continuing education programs – depending on the funding program, up to 100% funding is possible.
The requirement is that you live or work in Germany. Our funding opportunities are not available outside of Germany.

Funding opportunities are available for career starters, job seekers, career changers, startups, experienced professionals, those seeking career advancement, and specialists.