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Achieving AI Quality in Practice 2022

Kategorien: Introduction course
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Über den Kurs

Dear participants,

welcome to our course “Achieving AI Quality in Practice”!

We are thrilled to have you join us on exploring the use of data-driven systems with a focus on new challenges. An open mind and a curious spirit is everything you need to complete this course. The first four modules require no or not much technical background. Module 5 is a bit more technical, suited best for learners with a background in software engineering, AI and data sciene, but designed to be also useful for other roles, e.g. product owners, to learn the technical language and background in creating AI systems.

Artificial Intelligence, or AI, has become an integral part of our lives, revolutionizing industries and shaping the way we interact with technology. However, the key to unlocking its true potential lies in ensuring the quality of AI systems. In our modern world, where AI ranges from various sectors- namely from healthcare and finance to transportation and entertainment- understanding AI quality is not only something optional, but it has become an indispensable skill. By grasping the principles and techniques behind AI quality, you will position yourself as a frontrunner in the realm of sustainable innovation. This knowledge empowers you to contribute to the development of AI systems that are not only intelligent but also reliable, robust, and trustworthy.

Explore AI Quality
We will explore throughout the course three examples of real-world use of AI: Autonmous vehicles, a large language model powered insurance bot and a production setting within a greenhouse. Throughout this course, we will unravel the essential elements of AI quality, including technical, regulative and normative considerations. To explore practical techniques and industry´s best practices, we have included multiple perspectives such as those of a manager or project lead and of a operations lead, that will equip you with the knowledge and skills needed to evaluate, enhance, and ensure the quality of AI systems.

Complete course
Our online solutions are series of courses in which you gain an overview. To get started, subscribe directly to the course and review its modules and select the one you want to start with. When you subscribe to a course, you automatically subscribe to the entire modules. It’s okay if you only want to complete one module- you can pause your learning or cancel your subscription at any time. Go to your learner dashboard to track your course enrollments and progress.

Earn a certificate
Once you have completed all modules you will receive a certificate that you can share with potential employers and your professional network

Contact us
Please reach out to our team of experienced instructors if you have questions: They are passionate about sharing their expertise and guiding you on this transformative learning journey.

Start the course
So, get ready to expand your horizons, challenge your intellect, and unlock the insights of AI quality. You can start the class now, enjoy! Best, AI Quality & Testing Hub Team

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Was werde ich lernen?

  • Knowledge on basic prerequisites to comply with coming global regulation of AI.
  • Estimate impact on your products for market compliance.
  • Understanding what is needed to achieve AI Trust Label conformity.
  • Skills to determine if development fits within AI Quality requirements.
  • Understanding of testing frameworks, tools and benchmarks.


Defining the scope, context and criteria of AI quality ​
In this module, you will first acquire a basic understanding of what AI quality means and why it is important. For this, we will show you multiple angles of AI quality, both on the technical and normative and ethical side including their possible quality criteria. Enjoy!

  • Introduction
  • Overview on AI Quality
  • Technical performance measures and robustness​
  • Ensuring Privacy
  • Non discriminative, fair and minimization of other biases
  • Transparency and explainability
  • Safety and security
  • Accountability, auditability and liability
  • Wrap-Up
  • Defining the scope, context and criteria of AI quality

International AI Regulation and its impact on product development
In this module, you will gain an overview on the international landscape of AI regulations and standards with a deep-dive into the European standards (EU AI ACT). You will understand the risk-based approach and the risk classes with lists and examples, including horizontal vs vertical and harmonized standards. Enjoy!

Trust Classes, AI Trust Label and standardization​
In this module, you get insights into a pre-standardization approach developed by industry, namely the AI Trust Label. By looking closer into the trust classes and properties, you will understand the indicators and their assessment and their aggregation. Enjoy!

Managing quality within the AI product life cycle​
In this module, you will learn about AI governance from a manager perspective by getting hands on concrete technicalities and tools for adequate data governance, data management procedures and procedural tools to be implemented by providers of AI systems. You will know how they provide operational and process-related guidance. Enjoy!

Operationalizing AI: Accessing and testing properties of AI enabled systems
In this module, you will get an overview on how developement and operation of AI systems work by understanding the viewpoint of AI Engineers and MLOps specialists. For this, we share and compare tools and metrics for trustworthy AI with you, so that you get a feeling for technical issues such as bias detection, transparency, performance, robustness, safety and security. Enjoy!

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