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Description
As a DevOps I would like to be able to serve the models in a more flexible way instead of being vendor locked with AzureML
Expected behavior
Describe what you expected to happen.
Frameworks Considered
Flask - AzureML endpoints serve models using Flask
Torchserve - GCP Vertex and AWS Sagemaker uses Torchserve
LitServe - Pytorch Lightning project based on FastApi with optimizations for ML model serving
The text was updated successfully, but these errors were encountered:
Description
As a DevOps I would like to be able to serve the models in a more flexible way instead of being vendor locked with AzureML
Expected behavior
Describe what you expected to happen.
Frameworks Considered
Flask - AzureML endpoints serve models using Flask
Torchserve - GCP Vertex and AWS Sagemaker uses Torchserve
LitServe - Pytorch Lightning project based on FastApi with optimizations for ML model serving
The text was updated successfully, but these errors were encountered: