AI Experimentation and Production Readiness

This unit guides learners from research and experimentation toward production-ready AI products by teaching how to synchronize development and model lifecycles, operationalize models, and manage risk. Students will examine interpretability, ethics, monitoring and drift mitigation, and the AI-UX product manager role to prepare for implementation and scaling in subsequent units.

Learning Objectives

• Analyze the SDLC and MDLC stages and produce a synchronization plan that identifies integration points, dependencies, handoffs, and success criteria for transitioning models from research to production
• Design an AI experimentation plan using the problem analysis framework that defines hypotheses, datasets, evaluation metrics, interpretability needs, and rollout criteria for a generative AI or deep learning use case
• Evaluate model-related risks—including ethical, legal, interpretability, and drift risks—by conducting assessments and proposing monitoring, mitigation, and governance strategies aligned with business objectives
• Demonstrate operational readiness by developing a model-ops checklist and an AI-UX product manager brief that include monitoring strategies for model drift, procedures for model updates, and a phased implementation roadmap