Leverage top XAI frameworks to explain your machine learning models with ease and discover best practices and guidelines to build scalable explainable ML systems
 
Key Features:
    Explore various explainability methods for designing robust and scalable explainable ML systems
    Use XAI frameworks such as LIME and SHAP to make ML models explainable to solve practical problems
    Design user-centric explainable ML systems using guidelines provided for industrial applications