How it works

 

You can find the full documentation here : https://docs.ovh.com/gb/en/ml-serving/

 

Step 1 : Connect to OVHcloud control panel

Connect to OVHcloud control panel, and find "ML Serving" in the Public Cloud menu.

 

 

 

Step 2 : Initialize a namespace

This namespace will be linked to your Public Cloud object storage bucket and will contain your deployed models

 

 

Step 3 : Deploy machine learning models

You can deploy your own models or pretrained ones in our catalog.

 

 

We are compliant with standard formats,, such as TensorFlow HDF5/SavedModel, PMML, ONNX and so on.

 

Step 3 : create a security token

By default, no one can query neither manage your deployed models. You need to create a token to do so.

 

 

Step 5 : Done ! Access your swagger to query your models

You can now access to your swagger in order to know how to interact with your model. As the swagger is generated with your model input, you can easily generate a client library to plug it to your code.

OVHcloud will scale automatically the infrastructure behind.

 

 

 

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Features and benefits

 

Interoperability

Include your model in any framework or languages, you are not bounded to the framework used to create your ML models.

 

Versioning

Retrace your steps with integrated versioning.

 

Auto Scalable

Based on amount of calls you receive and metrics we gather, such as CPU, RAM consumption and latency, we will scale your infrastructure to always provide a reliable and performant service.

 

API Endpoint

Deployed models are available through and HTTP API Endpoint along with a Swagger tailored to your actual model inputs.

 

Monitoring / Healthchecks

Powered by OVHcloud Observability, monitor your models usage, load and liveness, never feel caught off guard by an unavailable serving.

 

Multi-backend : TensorFlow / PMML / ONNX & more to come !

You can bring Machine Learning models made with various tools, such as Tensorflow, Scikit-Learn, PMML language or ONNX hub. The platform is backend agnostic and no format/framework is out of reach and can be added to the platform to fit your needs.

 

High Availability

Your models will be deployed by default on multiple instances, powered by Kubernetes.

 

Granular security & security policies

Manage access to models and deployments using Role Based Access Policy (RBAC).

 

Rolling updates

Deploy your new model version without any service interruption.