This unit transitions learners from AI foundations into practical productization by covering experimentation mindsets, research-to-production workflows, and the operational practices required for reliable AI systems. Students investigate lifecycle synchronization (SDLC/MDLC), model interpretability, MLOps, AI-UX product management, generative AI use cases, and ethical and monitoring strategies needed to move models from prototype to production.
Learning Objectives
- Analyze how the software development lifecycle (SDLC) and model development lifecycle (MDLC) align and influence decisions across research, experimentation, and production stages
- Apply problem-analysis and experimentation frameworks to design reproducible AI experiments, define success metrics, and plan transitions from research to production
- Demonstrate model operations practices including interpretability techniques, versioning, deployment orchestration, and monitoring strategies to detect and manage model drift
- Evaluate ethical, user-experience, and business trade-offs—such as the innovator’s dilemma and AI-as-invisible-interface—when creating strategic implementation plans for AI products
Curriculum
- 1 Section
- 19 Lessons
- 30 hours
Expand all sectionsCollapse all sections
- Unit 2 — AI Product Development & Operations19
- 1.1A Walk Through the AI Landscape20 mins
- 1.2Key Aspects of an Experimentation Mindset20 mins
- 1.3Stages of SDLC20 mins
- 1.4Strategies for Developing a Research Mindset20 mins
- 1.5Strategic Planning for AI Implementation20 mins
- 1.6The Significance of Model Interpretability20 mins
- 1.7Key Components of Model Operations20 mins
- 1.8The Role of an AI-UX Product Manager20 mins
- 1.9Practical Applications of Generative AI20 mins
- 1.10Problem Analysis Framework20 mins
- 1.11Deep Learning and Generative AI—The Frontiers of Innovation20 mins
- 1.12Experimentation in AI Projects20 mins
- 1.13Synchronizing the MDLC and SDLC for Seamless Integration20 mins
- 1.14Transitioning from Research to Production20 mins
- 1.15Understanding the Innovator’s Dilemma in AI20 mins
- 1.16Ethical Considerations in AI Models20 mins
- 1.17Strategies for Monitoring and Managing Model Drift20 mins
- 1.18AI as the Invisible Interface20 mins
- 1.19Endnote20 mins
Instructor
Free
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