Responsible AI, Explainability & Deployment
By External Provider
About this course
Build and deploy production-ready AI decision systems that are optimized, explainable, and compliant with enterprise ethics and privacy standards. In this course, you will design a dynamic pricing system that integrates price-elasticity modeling, real-time trigger logic, and automated decision pipelines. You will then layer in fairness analysis, differential privacy, and SHAP-based explainability to meet the rigorous demands of responsible enterprise AI. You will apply mixed-integer programming to optimize pricing decisions, configure real-time streaming pipelines, and validate system performance against service-level agreements. You will also evaluate bias-mitigation approaches, implement privacy-preserving techniques, and produce compliance documentation that satisfies GDPR and CCPA requirements. Each skill builds toward a capstone project that mirrors what senior AI engineers deliver in production environments — giving you a portfolio-ready system that demonstrates your ability to move from raw data to responsible, automated, explainable decisions.
What you will learn
- Apply fairness metrics and bias-mitigation techniques to AI pricing models and document the accuracy trade-offs for enterprise stakeholders.
- Implement differential-privacy mechanisms and evaluate whether privacy controls preserve the analytical utility required for marketing segmentation.
- Generate and compare SHAP and LIME explanations for black-box pricing decisions, producing visuals interpretable by non-technical stakeholders.
- Design and validate a real-time dynamic pricing system with optimization models, automated triggers and compliance-ready guard-rail enforcement.