As the use of various types of “artificial intelligence” technologies continues to grow in software development, the importance of robust testing for AI-enabled systems cannot be overstated.
This webinar will look at the nuances of testing AI systems, highlighting how it diverges from traditional software testing. We'll explore innovative techniques specifically designed for AI-enabled systems and discuss the unique risks and issues it presents.
Whether you're a testing veteran or just starting your career, this session will equip you with the knowledge and tools necessary to navigate the complexities of AI testing with confidence and precision.
Key Takeaways:
- Understanding the fundamental differences between testing AI-enabled systems and traditional software systems.
- Exploring specialized techniques for testing AI models, including validation of machine learning algorithms and data integrity.
- Identifying and mitigating the unique risks and issues inherent in AI-enabled systems, such as bias, transparency, and unpredictability.
- Learning best practices for continuous monitoring of AI models to ensure ongoing accuracy and reliability.
- Gaining insights into real-world case studies and practical applications of AI testing strategies.
Join us to unlock the keys of effectively testing AI-enabled systems and ensure your AI-enabled applications deliver reliability and quality.
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