SeldonIO
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Seldon core converts your ML models (Tensorflow, Pytorch, H2o, etc.) or language wrappers (Python, Java, etc.) into production REST/GRPC microservices.
Seldon handles scaling to thousands of production machine learning models and provides advanced machine learning capabilities out of the box including Advanced Metrics, Request Logging, Explainers, Outlier Detectors, A/B Tests, Canaries and more.
We provide optimized model servers for some of the most popular Deep Learning and Machine Learning frameworks that allow you to deploy your trained model binaries/weights without having to containerize or modify them.
Send API requests to your deployed model
Every model deployed exposes a standardised User Interface to send requests using our OpenAPI schema.
This can be accessed through the endpoint http://<ingress_url>/seldon/<namespace>/<model-name>/api/v1.0/doc/ which will allow you to send requests directly through your browser
These are Seldon Core main components:
Reusable and non-reusable
to containerise models
CRD and
for advanced inference graphs
as well as integration with third-party systems:
Kubernetes Ingress integration with and
with
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To learn more about Seldon refer below links:
https://docs.seldon.io/projects/seldon-core/en/latest/index.html\
docs.seldon.io
https://github.com/SeldonIO/seldon-core\
https://github.com/SeldonIO/MLServer\
Basically Seldon Core is the "orchestrator" that takes your model, deploys it on to a server, collects metrics and logs and creates services and routing over the top. MLServer is the "engine" that actually handles executions of that model during inference. Seldon Core uses MLServer as a runtime to deploy your models on.
Another big difference is seldon-io core uses Flask under it's backend (for non-parallelization).
While ML-server is build with FastAPI, so our APIs with seldon can work on top of it as well.