Delve into the complex world of testing Machine Learning systems, uncovering potential risks and failure modes.\
Explore the distinctive challenges posed by testing Machine Learning systems. Understand the nuances of data-driven, evolving algorithms and their impact on testing strategies.
Discover the criticality of data quality and bias assessment in ML testing. Learn how skewed or incomplete data can lead to skewed results and compromised models.
Dive into the challenge of ensuring model robustness and generalisation. Explore strategies to validate ML models against diverse inputs and real-world scenarios.
Understand the importance of model interpretability and explainability. Learn how to address the 'black box' nature of ML systems to gain stakeholder trust.
Explore the need for continuous monitoring and adaptation of ML systems. Discover how models degrade over time and how to implement timely adjustments.
Know strategies for effective ML testing. Highlight the significance of comprehensive testing, collaborative efforts, and staying updated with evolving ML techniques.