I am evaluating DS WorkBench, so appreciate your views on how Data Science Workbench supports the following:
- Model Parallelism: Support of Horovod out of the box?
- Platform Agnostic: Any platform specific implementation. How easy to migrate to other platforms?
- Language Supported: Python, R
- Framework Supported: Scikit Learn, XGBoost, Tensor, Keras, MXNet, etc
- Collaborative Environment: Can share experiments, models with teams.
- Integration with MLFlow: To creating platform agnostic model formats: Edge, Tensor Serving, SparkML, Pickle, etc.
- CI/CD Pipelines support
- AB Testing