Fast-Track Your AI-ML Model Integration
summary
Claims a practical MDLC that starts productization day one and often cuts time-to-market by 50%. Shows two concrete projects (J.P. Morgan alerting pipeline and ING KYC real‑time API), scaffolds an inference server bridge layer, runs as a Docker container on Cloud Run, and uses a schema registry plus a dummy orchestrator to exercise integration before backfilling the final model. Demonstrates what was built, how it handled missing IDs and availability, and that the approach accelerates deployment.
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chapters (10)
- 0:00Introduction
- 0:48AI model integration failures
- 3:35Conway's law barriers
- 7:08Traditional model lifecycle
- 9:59Flipping development plan
- 12:05Day one integration architecture
- 16:43Real-time KYC example
- 20:27Inference server bridge
- 26:55Key takeaways
- 28:28Q&A
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