ML Serving FAQ
ML Serving FAQ
Last updated 16th June, 2020
Here are the most frequently asked questions about OVHcloud ML Serving.
You can order the ML Serving solution via OVHcloud EU or CA Control Panel. However, the product is not available via the OVHcloud US Control Panel.
For the moment, only the following region is currently available :
EU WEST FR 1: automatic deployment of servers from Gravelines (GRA).
Other regions are currently coming up because OVHcloud wants to deploy ML Serving in all Public Cloud regions.
We provide you with the auto-scaling functionality which is based on the amount of calls you will receive on the endpoint API. Depending on this, we will add or remove nodes.
For example: you have few calls and it runs on 1 node, then a peak comes to reach more than 60% CPU usage. Automatically, we will deploy an additional node and so on.
For more information, we invite you to read the following guide.
ML Serving allows you to deploy ML models in a few steps.
If your model is compliant, it will work. At the moment, there are no other limitations.
For more information about compliance, we invite you to read the following guide.
It is possible to deploy a model via Control Panel or API. For more information, we invite you to read the following documentation.
Models need to be stored inside a OVHcloud Public Cloud Object Storage container.
During your namespace creation, you will be prompted to "attach" a OVHcloud Object Storage container to store them
Then, you will push your models inside this container.
ML Serving is charged by the minute. Also, you pay the amount of "power" used, for each minute.
We have pricing per nodes, consuming RAM and per vCore.
Example : you deploy a model on 2 nodes, for 80 minutes. Each node has a profile and a price.
You will have to pay :
Deployment of the model, management of deployed models, versioning, monitoring are all included in the ML Serving offer.
Object Storage and Private Registry are not included in the ML Serving offer.
For more information, we invite you to read our specific conditions for Public Cloud service.
We are working to provide metrics in the Control Panel and also provide an Insight token.
This new solution is designed to simplify our customers’ lives. OVHcloud provides an easy way to deploy Machine Learning templates.
To do this, OVHcloud provides an HTTP API endpoint for each model, with versioning, monitoring, and autoscaling.
To learn more about using ML Serving and how to create a cluster and process your data, we invite you to look at the ML Serving documentation page.
Please feel free to give any suggestions in order to improve this documentation.
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