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Version: 1.39

Create a Deployment

A Deployment represents an instantiation of a machine learning model that is hosted on physical hardware. It serves as a reference to the actual deployment taking place on the underlying infrastructure. Deployments always include a model, and can optionally also host an explainer, transformer, or both. Each Deployment is equipped with an endpoint, enabling communication with the model to retrieve predictions and explanations. Every Deployment has an owner, who assumes responsibility for its management. Initially, the user creating the deployment automatically becomes its owner.

Creating Deployments is a straightforward process that can be completed in a few simple steps, using the Deployment API, Python Client, or UI.

To create a Deployment using the UI, head to Deployments. The default deployment flow uses KServe as a deployment service. If you want to use AWS Sagemaker as a deployment service you can learn more in this in this article, or if you want to use Azure Machine Learning as a deployment service you can learn more in this in this article.

tip

For large Repositories, we suggest creating Deployments using the Python Client to speed up the process.

Prerequisites​

1. Repository​

Choose to use an existing Repository. Deeploy will list currently linked Repositories in a dropdown. Select the repository you want to use for your Deployment.

After selecting a repository, Deeploy will list the branches and commits. Select the branch and commit that you want to deploy.

2. Deployment​

Name your Deployment. Optionally, add a short description. If you have added metadata using the metadata.json file in the Repository, you can retrieve and review the metadata.

You can also choose to switch the deployment service for the Deployment. Untoggle Use default deployment service, and select the service you want to use. Change the default deployment service on the Workspace Integrations page.

3. Model​

Select the model framework that you have used to train the model. Supported frameworks, versions, and examples can be found in Supported Framework Versions for KServe.

Alternatively, deploy a Custom Docker Image, this article guides you through the steps of deploying your Custom Docker Image, including template generations for easy custom Docker image creation. The only additional requirement is to use a webserver to expose an endpoint.

For more information on the advanced model options, see advanced Deployment options.

4. Explainer​

Select the explainer framework that you have used to train the explainer. For detailed information see Deploying an explainer. Supported frameworks, versions, and examples can be found in Supported framework versions for KServe.

For more information on the advanced explainer options, see advanced Deployment options.

5. Transformer​

Select the transformer framework that you have used to train the transformer. Supported frameworks, versions, and examples can be found in Supported framework versions for KServe.

For more information on the advanced transformer options, see advanced Deployment options.

6. Compliance​

Fill in the compliance insights to comply to responsible AI standards. Filling in the compliance insights is completely optional, but advisable when dealing with high risk applications. Checklist templates cannot be fully assesed yet, and are therefore read-only.

7. Deploy​

Click Deploy, Deeploy will now initiate the automated deployment process. You will be directed to the Events page where you can monitor the progress of the deployment steps.