Getting Machine Learning applications into production using MLOps and CD4ML
summary
87% of data science projects never reach production, so CD4ML lays out a concrete six-step pipeline — model building, evaluation, productionization, testing, deployment, monitoring — that treats data engineering, the trained model, and the API as three concurrent projects to orchestrate. Emphasis falls on discoverable data via a catalog, automated tests and accuracy gates, versioned models and datasets in an artifact repository, and one-click deploy enabled by continuous delivery orchestration so deployments become a business decision rather than a technical blocker.
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chapters (6)
- 0:00Introduction
- 4:48CD4ML
- 7:07Three Projects
- 9:07Vertical Slices
- 10:27Discoverable Accessible Data
- 12:47Wrap Up
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