From Prototype to Production Building a Scalable MLOps Platform from a Reference Arch
summary
A concrete reference architecture connects training pipelines, a model registry, a feature store and online scoring behind an API, with orchestration via Airflow/Kubeflow or MLflow, CI/CD for models and infra, and logging plus business metric monitoring. Generative AI is framed as AutoML to be consumed or fine‑tuned, not rebuilt. Advice is to start small, separate model lifecycle from app lifecycle, and retrain on operational data weekly or monthly as needed.
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chapters (8)
- 0:00MLOps introduction
- 4:46MLOps beyond model code
- 6:12Paradigm shift in teams
- 11:18Reference ML architecture
- 23:44MLOps tool selection
- 30:13Scaling MLOps
- 38:19Model deployment best practices
- 40:30Summary
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