An AI and ML portfolio for production-minded leaders.
This portfolio is organised around proof themes rather than isolated demos: business problem, system design, data and ML approach, measurement, production readiness, and lessons for executives.
Recommendation and decisioning systems
Product recommenders, next-best-action, churn prevention, customer lifecycle decisioning, and AI search in ecommerce contexts.
Measurement and experimentation
Incrementality, A/B testing, statistical significance, contextual bandits, and guardrail metrics for AI-enabled products.
Enterprise AI in production
Governance, MLOps, FinOps, delivery models, monitoring, adoption, and the operating model required to move beyond POCs.
Case-study standard
Each mature portfolio asset will follow a consistent structure: executive summary, system context, approach, measurement, production readiness, artifacts, and lessons for senior leaders.
The goal is not to look like a Kaggle gallery. The goal is to show how AI systems create durable business value when product, data, engineering, and governance work together.
Keep reading from this thread
From 491 indexed pages and articles.
- Insight Insights Essays and executive notes on enterprise AI in production, recommendation systems, ecommerce AI, measurement, and AI operating models Related by production
- Insight Recommendation Systems in Production How recommendation systems become production decisioning systems through signals, ranking, constraints, feedback loops, and experimentation Related by production
- Page Robin Cartier – Enterprise AI Product Leader Enterprise AI product leadership for teams turning AI pilots, recommendation systems, and decisioning loops into measurable operating capabilities Related by production