One key component of MLOPS is continuous training. Which means the end to end training is put in a pipeline which can be triggered, versioned and metadata of the pipeline can be tracked. Thus enabling retraining of the model without lots of manual efforts. Which tool/package do you use for creating such a training pipeline? I am looking for simple tool with following criteria

  1. Using only python (i.e. docker is not mandatory like kubeflow)
  2. Dev have lot of flexibility and is not created for specific tasks like nlp, regression, classification etc.
  3. Little learning curve/new syntaxes but is simple to understand and use
  4. Platform or cloud agnostic

I came across the below and didn't find it very useful due to the mentioned reason

  1. Kubeflow - every component of the pipeline need to be in a separate docker
  2. Kedro - require learning lots of new syntax etc

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