Deploy Preset Models

Learn how to deploy preset models

Last updated 10th January, 2020.


Deploying models is the main feature of the ML Serving. This guide provides step by step instructions on how to deploy a preset model.



Step 1 - Reach model page

Inside your ML Serving namespace page there is a dedicated tab for managing models : Models.

Model menu

You can start the deployment of a new model by clicking the Deploy Model button.

Step 2 - Select the preset model

In this guide, we explain how to deploy a preset model. Just select preset model and click Next.

Select preset model

Then you have to choose which preset model to deploy.

Choose which preset model

Just select the chosen one and click Next.

Step 3 - Choose a name for your model

That name identifies your model among others on your namespace.


Once you chose a name, click the Next button.

Step 4 - Choose your auto-scaling options

A model is composed of one or several running instance(s). These instances are automaticaly scaled by the ML Serving depending on the input load of your model. That step allows you to configure the minimum and maximum number of instances that you want to run.


During the beta phase, the auto-scaling feature is not customizable and we reserve the right to remove unused models

Step 5 - Choose your instances configuration

Each model instance is related to a CPU & RAM flavor. You can choose the wanted flavor among a list of existing ones.


During the beta phase only one type of instance is available and is free of charges. Additional flavors will be created to fit specific needs.

Step 6 - Wait for model deployment

The ML Serving sequentially performs the following tasks :

  • The building of the model : you should see the build status as Building.
  • The deployment task : you should see the API Status asPending.


When everything is up and running you see the build status as Deployed and the api status as Running. The URL where to reach your model is also displayed so you can start requesting it.


Going Further

  • You can check the official documentation of the open source product ML Serving about Models

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