What are the different alternatives for ML Model Deployments and Pipelines and Pros and Cons for each.
I have came across few
- GraphPipe (https://oracle.github.io/graphpipe/#/) from Oracle
- Tensorflow Serving, TFX (https://www.tensorflow.org/tfx/) from Google
- Mlflow (https://mlflow.org) from databricks
- kubeflow (https://www.kubeflow.org/) from Google
- Seldon (https://www.seldon.io/)
I am looking towards some perspectives such as features, community support, ease of use, framework support such as (PyTorch, Tensorflow, etc), scalability.
I have came across few
- GraphPipe (https://oracle.github.io/graphpipe/#/) from Oracle
- Tensorflow Serving, TFX (https://www.tensorflow.org/tfx/) from Google
- Mlflow (https://mlflow.org) from databricks
- kubeflow (https://www.kubeflow.org/) from Google
- Seldon (https://www.seldon.io/)
I am looking towards some perspectives such as features, community support, ease of use, framework support such as (PyTorch, Tensorflow, etc), scalability.