Containerization

Ai-APIᵀᴹ Engine: Deploy

Deploying Models Guide

Before starting to read this guide, it is recommended for you to read the Build Models Guide of the Ai-APIᵀᴹ Concept.

After a model is build trained and containerized, comes the deploying stage.

Now this is the part where our Ai-APIᵀᴹ Engine comes into play.

Our Workspace have a Spawned Deplyment section and the sidebar at the left side of your screen has a menu button called Containerization which will lead you to our Ai-APIᵀᴹ Engine, Containerized model status and Pipeline status

Figure 1: Spawned Deplyments

Note:

If in the Building phase you have already chosen the auto-deployment feature provided by our Zeblok CLI, you will be able to see the your model under the "Spawned Deployment" section deployed as an AI-API.

For Manual deploying process, the steps are as following:

• Click on Deplyment Button on the left hand menu and you will see the contanerised model in "ready" state which you can easily deply as AI-API by clicking the Deply AI-API button under Actions.

Figure 2: Deplyments Tab

• Then comes the selection of namespace (only if you are a member of one).

Figure 3: Namespace Selection

• Third step is to select the location where you want to deploy it.

Figure 4: Select DataCenter

• If you selected an Edge Datacenter in the previous step, you have an additional step where you are given an option to choose whether you want your deployment to be created on the HUB or any SATELLITE Node.

Select HUB or SATELLITE NODE

• Name your Deployment and click on Create.

Figure 5: Name of Deployment

You will be redirected to the Workspace while the deployment is being done in the background. On successful deployment, the status of the deployment will change to DEPLOYED.

Figure 7: Deployment Successful

Note: On successful deployment, when the status is deployed, you are now given an option to monitor various performance metrics as highlighted in the images below.

Performance Monitoring