Machine Learning Deployment Frameworks

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.