Connecting Model Work to Business ROI in H2O
- Predictive AI
- H2O Driverless AI
- Enterprise h2oGPTe
This lesson connects the technical work of building a model back to measurable business value. It shows how workspaces and projects can be described in terms of the business goal they support, and how Driverless AI can optimize a custom scoring function, such as a profit function that accounts for intervention cost and customer lifetime value, instead of a purely statistical metric like AUC. It covers tagging models with business metadata, like the owning business unit, to support portfolio management, and shows a super agent drafting a business case from model performance data, plus API syncing of model events into tools like Jira.
Key takeaways
- How a custom scoring function can optimize for profit or customer lifetime value instead of a statistical metric like AUC
- Why tagging models with business metadata, like the owning business unit, supports portfolio management
- How a super agent can draft a business case and ROI analysis directly from model performance data
- What model lifecycle events, like a completed training run, can sync automatically into tools like Jira or ServiceNow
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