Assessing robustness of AI systems is a crucial part of their development and deployment. Here at BSI, we have developed a course that explores in depth the importance and impact of implementing robust deep learning systems, following the Standard ISO/IEC 24029-1:2021. We deep dive into the core of the standard, exploring all its technical parts and the corresponding actionable contents.
How will I benefit?
This course will help you:
- Build a toolset of methods that can help identifying robustness issues
- Understand the different types of data perturbations, and their use in the creation robustness test data sets
- Design workflows to detect and address robustness concerns
- Take steps to ensure that the assessment of robustness is part of the development and deployment of AI systems involving neural networks
Who should attend?
- Data Scientists
- Data Analysts
- Data Engineers
- Machine Learning Engineers
- AI architects
What will I learn?
Upon completion of this course, you will be able to:
- Apply the methods provided by the standard to detect robustness issues arising in the development and deployment of a deep learning system
- Describe the main principles behind data perturbation and abstract interpretation
- Construct protocols to assess robustness of a neural network
What is included?
On completion, you'll be awarded an internationally recognized BSI Training Academy certificate
