MLflow Pipelines: Accelerating MLOps from Development to Production

[@ousmanetraore597]

Why every one using yaml everywhere? with no code completion, difficult to test/validate, every thing needs to be in a single huge file because we can't use function abstraction ? This is fine for simple "transform"-> "train" -> "test" pipeline, but become very hard for complexe ones. I prefer the Airflow way of defining pipelines with Python code.

[@risebyliftingothers]

managing airflow infra in house is a task in itself. flexibility comes at a cost. and btw yaml is what kubernetes thrives on and most of infra-as-code tools :)

[@bharathjc4700]

How do we move the artifacts to prodiution

[@swatikarot8272]

Love this. Thanks for the great session. 👍

[@geleshgomathil3274]

Notebook & Slides Link

[@jerryyang7270]

This is great!

[@rohitchatterjee2327]

this was a very good session

[@sitrakaforler8696]

Cool

[@LavaKafleNepal]

wow awesome