Learning the fundamental elements to build effective AI/ML products
summary
Cites Gartner that 80% fail to reach production and prescribes the CRISP-DM pipeline—business understanding, data exploration, feature engineering, modeling, evaluation, deployment—with concrete tactics: build quick prototypes during exploration, prefer handcrafted features before AutoML, measure business impact (fraud dollars saved, revenue vs profit), set monitoring alerts and retrain thresholds, ensure deployment parity across environments, involve field users early (predictive maintenance stalled until engineers joined), and avoid protected attributes in credit models to prevent bias.
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chapters (12)
- 0:00Intro
- 5:21Topic
- 6:10Why this topic
- 8:48Rulebased mindset
- 16:29Structured approach
- 18:45Objective phase
- 24:12Data understanding exploration phase
- 27:38Data wrangling feature engineering phase
- 29:47Modeling and evaluation phase
- 33:55Deployment and monitoring phase
- 36:31Collaboration
- 38:01Bias in model development
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