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@kemingy
Last active April 26, 2019 07:33
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MLflow

https://github.com/mlflow/mlflow

Pros

  • UI (tracking and comparing)
  • Run ML projects with different parameters
  • Serving
  • reproduce with conda config file

Cons

  • weak store (store in local file, slow to load)
  • ugly code
  • do not support save artifacts to user server
  • model serving is weak

Polyaxon

https://github.com/polyaxon/polyaxon

Pros

  • use Docker
  • UI
  • Notebook plugins
  • Tensorboard
  • Hyperparameters tuning

Cons

  • coupled to k8s
  • hard to deploy and debug

DVC

https://dvc.org/

Pros

  • built for large data version control
  • data saved in cloud or local or HDFS
  • support reproduce experiments with user histroy

Cons

  • not easy to use when user want to record the experiments
  • beta
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