Panel: Scaling AI successfully
summary
Scaling succeeds when companies stop treating AI as isolated pilots and adopt a data-first architecture with a central data hub, global metadata and governance so models can be reused across factories. A working effort builds a deployable computer-vision quality‑inspection framework that separates common vs configurable components; balancing that is key. Expect failure: roughly 20 pilots, 17 fail unless processes, roles (data engineer, ML engineer, MLOps, domain expert) and lifecycle explainability and trustworthiness are baked in. Emerging foundation models reduce labeling and cut replication effort.
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chapters (4)
- 0:00Start
- 0:27Scaling Ai Successfully
- 9:17What Are the Important Aspects To Have in Mind When When Scaling or Setting Up Such a an Ai Project
- 13:38Business Model
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