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BMA: Project, Product & Operations
Operationalizing Intelligence-First Products
Curriculum
1 Section
18 Lessons
30 hours
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Unit 4 — Operationalizing Intelligence-First Products
18
1.1
The Revolutionization of Generative AI
20 mins
1.2
Real-World AI: Bridging Theory and Practice
20 mins
1.3
Traditional AI vs. Generative AI
20 mins
1.4
Overcoming Common Challenges in Integrating the MDLC and SDLC
20 mins
1.5
Generative AI and Traditional AI within Scaling Research to Production
20 mins
1.6
Traditional AI vs. Generative AI
20 mins
1.7
Case Studies: Model Explainability, Interpretability, Ethics, and Bias
20 mins
1.8
Incorporating Model Operations into the Product Roadmap
20 mins
1.9
Balancing Generative AI and Traditional AI in Model Operations
20 mins
1.10
Endnote
20 mins
1.11
Case Studies of Successful MDLC and SDLC Integration
20 mins
1.12
Case Studies: Overcoming Initial Underperformance in AI
20 mins
1.13
Traditional AI vs. Generative AI in Model Explainability, Interpretability, Ethics, and Bias
20 mins
1.14
Traditional AI vs. Generative AI in Model Drift Management
20 mins
1.15
Case Studies: Real-Life Applications of AI as the New UX
20 mins
1.16
Traditional AI vs. Generative AI
20 mins
1.17
Case Studies: Model Operations
20 mins
1.18
Conclusion: The Dawn of Intelligence-First Product Creation—A New Chapter in Human Innovation
20 mins
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