Building the Business case for ML Platforms
summary
Argues that an ML platform proves value by concrete outcomes not promises: reduced deployment time 6–9 months to ~3 months and fewer duplicated models across teams, measured as faster time-to-user and lower engineering cost. Recommends prototyping with cloud APIs then evolving to a platform-as-product that enforces CI/CD, governance and human-in-the-loop fallbacks. Warns that at scale off-the-shelf APIs struggle with data ingest and latency — e.g. 17M users generating trillions of events drives bespoke integration choices.
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chapters (5)
- 0:00Panel introduction
- 2:24Panelist backgrounds
- 9:32Allocating ML investments
- 17:25Build vs. buy strategies
- 23:20Centralized vs. distributed
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