Toni Ramchandani from MSCI Inc. will cover essential testing techniques for AI/ML models. 
Learn about automated testing tools like DeepXplore and SHAP for identifying neural network inconsistencies and ensuring model transparency.
Not registered yet? Don’t miss out—secure your free seat and register now.
Already registered? Share your questions in the thread below 
Hi there,
If you couldn’t catch the session live, don’t worry! You can watch the recording here:
Additionally, we’ve got you covered with a detailed session blog:
Here are some of the Q&As from this session:
How can QA teams effectively validate the performance and reliability of models, especially when dealing with complex data and evolving algorithms?
Toni Ramchandani: QA teams can validate AI models by mastering Python and key libraries like Pandas, understanding AI concepts and algorithms, and staying updated with the latest advancements. They should focus on continuous learning, implement testing strategies, and actively collaborate with developers to handle complex data and evolving algorithms effectively.
How can SHAP be effectively utilized to interpret model predictions and enhance transparency in AI/ML models?
Toni Ramchandani: SHAP is a great tool for interpreting model predictions by explaining the impact of each feature on the output. It assigns weights to different features, showing us which ones influenced the decision the most, like why a model identified an object as a ‘cup.’ While not perfect, SHAP is currently the best library we have for enhancing transparency and understanding AI decisions.
Here are some unanswered questions that were asked in the session:
How do you test the interpretability and explainability of AI-ML models using automated tools?
Necessary components for automating the testing of AI - ML model?
What are the best practices for automating the retraining and validation of AI-ML models to ensure they remain effective as new data becomes available?
What are the strategies for automating the testing of AI/ML models to ensure they perform reliably across different data sets and scenarios?
When should manual testing be used in addition to, or in replacement of, automated testing of AI/ML models?
What methods is the best way to ensure a proper AI-ML model testing?
What are the most effective and widely-used tools, frameworks, and methodologies for implementing automated testing and quality assurance processes specifically for artificial intelligence and machine learning models?
What kind of strategy we should use for automating the testing of AI/ML?
What are the unique challenges in automating the testing of AI and machine learning models compared to traditional software?
What specific strategies or frameworks can be used for automating the testing of AI-ML models to ensure their accuracy, reliability, and performance?
What are the most effective frameworks and tools for automating the testing of AI-ML models, and how do they differ from traditional test automation tools?
How do we ensure automated tests for AI/ML models cover all possible scenarios and edge cases?
How can organizations ensure the reliability of AI/ML models while balancing security and performance through robust testing?
How you can use AI/ML in the daily routine?
How can effective governance help with improving automated testing of AI and ML models?
How do you see the future of AI/ML model testing evolving to address more complex challenges like hallucinations and ensuring model robustness?
