Masterclass: The right path to ML
summary
Claims a concrete playbook: start with data capture and dashboards, prove prediction before automating, and move iteratively from MVM (minimum viable model) to MVP and beta into production with monitoring and retrain hooks. Offers concrete metrics and risks: 85% of projects fail and modeling consumes only 20–25% of engineering effort. Recommends backtesting unseen holdouts, stress and security tests, fairness checks, simple first integrations (dashboard over SAP), and automated redeploy when drift degrades performance.
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chapters (4)
- 0:00Start
- 15:29Complexity in an Al project
- 26:35From a model to a solution
- 31:05What do we want to validate ?
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