An Only One Step Ahead Guide for Machine Learning Projects
summary
Practical checklist: find and interview the data owners until you locate the relevant tables, collect ~80% of useful fields, and build a strong data pipeline that ingests new data on schedule so iterations run fast. Pick one clear business metric (revenue or conversion), optimize one business metric aggressively, then start small — train simple models on clean slices, try many models quickly, combine only the few that actually improve the metric; watch for label drift (example: sudden product or instrumentation changes).
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chapters (13)
- 0:00Introduction
- 3:51What is Machine Learning
- 8:01What do you want to solve
- 11:32Data Pipeline
- 14:15Define Metric
- 16:55Start Small
- 18:29Try Everything
- 20:52purist view of methods
- 21:40making a good model
- 24:52pizza analogy
- 27:15garbage in garbage out
- 28:01juice ball
- 28:25baseball analytics
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