Navigating the AI Project Lifecycle
summary
A stepwise roadmap maps problem definition through data, modeling, deployment and long term maintenance with concrete checks: can the data be accessed and is it current, what baseline and business KPI the metric maps to, and who from the business must sit in on development. Lessons include 80% fail at maintenance after rollout, a dataset that is 100k → 10k usable after system changes, labeling drift of 10% wrong requiring relabeling, and an edge case at scale of 200M users with 8s→2s inference.
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chapters (14)
- 0:00AI project lifecycle intro
- 1:10AI execution pitfalls
- 2:35Why AI projects fail
- 5:29Data collection key questions
- 10:26Data labeling challenges
- 13:23High-quality data requirements
- 16:12Model training decisions
- 17:38Data set splitting for evaluation
- 18:44Model evaluation and KPIs
- 19:50Business team involvement
- 21:16AI ethics and robustness
- 23:04Model deployment strategies
- 24:50Production monitoring
- 26:56Summary & Q&A
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