我们的AI培训课程旨在为个人和组织提供必要的知识和技能，以践行最佳实践标准，并在复杂的AI监管环境中探索。在这个快速发展的领域，了解AI的道德、法律和合规方面对于负责任和可持续的部署至关重要。
我们的课程涵盖了广泛的主题，从AI监管的基础知识到专门的行业特定考虑因素，为参与者提供了开发 、部署和管理AI系统所需的工具。无论您是寻求提高专业知识的专业人士，还是旨在确保合规的组织，我们的 培训计划都是为了满足您的需求，并在人工智能驱动的时代为您提供支持。
Assessing robustness of AI systems is a crucial part of their development and deployment. Here at BSI, we have developed a short course that explores the main principles and the importance of implementing robust deep learning systems, as outlined in ISO/IEC 24029-1:2021.
On-demand - training that’s even more flexible
BSI’s on-demand courses are market-leading and available 24/7. Developed by top subject matter experts, they contain the same high-quality content you will find in our tutor-led training, but with the added benefit of being able to learn at your own pace and at any time.
Who should attend?
- AI经理
- 数据治理经理
- 数据科学家数
- 数据分析师
- 数据工程师
- 机器学习工程师
- 人工智能架构师
- AI Managers
- Data Governance Managers
- 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:
- Explain what robustness of an AI system is
- Recognize the different kinds of robustness issues that one can face in the development and deployment of deep learning systems
What will I gain?
On completion, you’ll be awarded an internationally recognized BSI training course certificate.
How will I learn?
This is an online, interactive on-demand course.
Courses are available 24/7 and you can learn at any time and from any place that suits you – you just need an internet connection.
You can learn as fast or as slowly as you want to. You can also take breaks at any time in the course and pick up where you left off when you are ready to continue
During the access period, you can go back and repeat parts or all the course
to refresh and reinforce what you have learned
The course content is both detailed and engaging, with explanations, activities, and knowledge checks to enhance your learning.
Course aim
The aim of this course is to give awareness of the concept of robustness in the context of deep learning systems, by exploring the dangers that can affect non-robust neural networks and proposing a workflow to detect and assess robustness issues, following ISO/IEC 24029-1:2021.
Prerequisites
The course assumes that delegates know the basic concepts of AI, and understand what a neural network is.
