MLflow Pipelines: Accelerating MLOps from Development to Production

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  • Опубліковано 1 гру 2024

КОМЕНТАРІ • 10

  • @ousmanetraore597
    @ousmanetraore597 Рік тому +6

    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
      @risebyliftingothers Рік тому +1

      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
    @bharathjc4700 2 роки тому +4

    How do we move the artifacts to prodiution

  • @swatikarot8272
    @swatikarot8272 2 роки тому

    Love this. Thanks for the great session. 👍

  • @geleshgomathil3274
    @geleshgomathil3274 Рік тому

    Notebook & Slides Link

  • @jerryyang7270
    @jerryyang7270 2 роки тому

    This is great!

  • @rohitchatterjee2327
    @rohitchatterjee2327 Рік тому

    this was a very good session

  • @LavaKafleNepal
    @LavaKafleNepal 2 роки тому

    wow awesome

  • @sitrakaforler8696
    @sitrakaforler8696 Рік тому

    Cool