Why current data science test processes in AI/ML are insufficient

Testing AI/ML-based applications is challenging due to the lack of a standardized approach. Current testing models often provide inadequate coverage and fail to address critical areas such as security, privacy, and trust.

Ensuring the quality of AI/ML-based projects requires a different approach from traditional testing. Testing strategies must identify bias and fairness issues in models while also focusing on transparency, interpretability, and explainability of decisions.

Understanding the key elements required to test these applications can significantly improve overall testing effectiveness and build confidence in deployment models.

This fireside webinar explores and evaluates the latest tools needed to test AI/ML models comprehensively.

Key takeaways:

• The lifecycle of an AI/ML project.
• Testing AI/ML projects versus traditional projects – a comparison.
• Test-first AI/ML processes and what they deliver, including edge cases, security, explainability, and privacy.
• A short demo of the Trigent testing product.