Bridging the gap: from Data Science to service
summary
Data science usually accounts for 20–30% of delivery effort while the rest is engineering, ops and coordination; projects succeed when teams verify raw data early, build a quick scikit-learn baseline to judge value, treat the trained model as an asset with reproducible inputs, and map model metrics to business KPIs in the contract. Teams share unknowns and risks openly, let specialists handle deployment and UX, and use lightweight visualizations to align expectations.
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chapters (8)
- 0:00Introduction
- 3:18Racing bread analogy
- 5:08Client needs and service fit
- 8:25Client communication practices
- 13:22Business value focus
- 17:21Team collaboration and alignment
- 22:44Data science delivery evolution
- 36:49Q&A
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