Models
Learn the concept behind serving engine models
Definition
Models in a ML Serving project refer to HTTP API endpoints that serve machine learning models.
There are two kinds of models :
- Preset models
- Serialized models
Preset models
A preset model is a model that has already been built and added by OVHcloud administrators of the ML Serving platform and is available for deployment on the fly.
Serialized models
A serialized model is a model that can be loaded from a file with a supported format.
Currently supported formats are:
- ONNX
- TensorFlow SavedModel
Instructions about how to export models can be found here:
Considerations
- Users choose to deploy a model inside one of their namespaces.
- Once deployed, each model is reachable from everywhere on the Internet from a generated url.
- Access control over models management and querying can be configured by the namespaces owner by creating access tokens.
Under the hood
Each model deployed inside a ML Serving namespace is actually a docker container built and pushed into the linked docker registry and then started inside the kubernetes namespace.
Going further
- You can check the OVHcloud documention on how to deploy preset models.
- You can check the OVHcloud documention on how to deploy custom models.
- You can check the supported compatibilities for custom models
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