The existing system using TorchServe is quite basic in terms of model management. A much more powerful system would involve:
- MLflow: For experiment management and model versioning
- TorchServe: The actual core serving can still be handled by TorchServe plugging into MLflow
- Airflow: For sequencing of scripting around training
The core of this is explained by another project in the class. They also have a GitHub repository that might be useful.
The existing system using TorchServe is quite basic in terms of model management. A much more powerful system would involve:
The core of this is explained by another project in the class. They also have a GitHub repository that might be useful.