AI Product Implementation & Scaling

This unit equips learners to implement, iterate, and scale AI products by combining model training, experimentation, continuous data strategies, and cross-functional deployment practices. Students will explore bias mitigation, multimodal and generative techniques, automation for model drift, and how to align technical trade-offs with business insights and product roadmaps.

Learning Objectives

• Analyze AI implementation frameworks and prioritize model-training and experimentation approaches based on resource, risk, and stakeholder constraints within a product roadmap
• Apply best practices for continuous data collection, retraining, and prototype iteration — including multimodal and generative methods — to meet defined performance targets
• Demonstrate effective cross-functional communication by producing a deployment plan that incorporates bias mitigation, explainability measures, and automated drift-management processes
• Evaluate real-world case studies and business insights to recommend product-level trade-offs among performance, fairness, explainability, and business KPIs