MLOps Deployment Patterns with Delta Lake and MLflow
summary
Three concrete deployment patterns appear: deploy model, deploy code, hybrid, and the clear claim is that the choice depends on which artifacts move and who can access production data. Delta provides Delta time travel (versioned Parquet plus a transaction log) to map exact data versions to models. MLflow logs models with a self-contained MLflow python-function flavor (conda.yaml) and a vectorized UDF that uses Arrow to scale inference, demonstrated end to end and shown to work.
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chapters (8)
- 0:00MLOps deployment patterns intro
- 1:25Why ML deployment is hard
- 5:57Delta Lake & MLflow overview
- 9:52Three main deployment patterns
- 12:57Data reproducibility with Delta Lake
- 17:05MLflow deployment optimizations
- 21:17CI/CD for automated deployments
- 27:37Key takeaways
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