Create a Deployment

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 backend. If you want to use AWS Sagemaker as a deployment backend 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.

Prerequisities

• You added a Repository that adheres to the requirements

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

In this step, you can add deployment metadata. Only the name is required, the description it optional. Extra metadata can be added by using the metadata.json file in the Repository.

You can also choose to switch the Deployment backend for the Deployment. To switch from your default backend to another one, untoggle Use default Deployment backend, and select the backend you want to use. You can change the default backend on the Workspace settings 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. The only additional requirement is to use a webserver to expose an endpoint.

Indicate whether or not your model should be deployed Serverless. Consult our guide for serverless deployments if you are unsure if serverless deployments are necessary.

For more information on the Deployment options see:

• Advanced configuration
• Serverless configuration
• Docker image credentials
• Blob credentials

4. Explainer

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

To decide whether or not your explainer should be deployed Serverless, please consult our guide for serverless deployments.

For more information on the Deployment options see:

• Advanced configuration
• Serverless configuration
• Docker image credentials
• Blob credentials

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.

To decide whether or not your transformer should be deployed Serverless, please consult our guide for serverless deployments.

For more information on the Deployment options see:

• Advanced configuration
• Serverless configuration
• Docker image credentials

6. Compliance

Fill in the compliance insights to comply to responsible AI standards. Filling in the compliance insights is completely optional, but advisible when dealing with high risk applications.

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.