What does deploying a model mean?

This article explains the purpose of deploying a machine learning model.

Your AI journey does not end with the training of machine learning models.

Next, we have to put the trained models in a production environment, exposing the model so they can generate predictions for business stakeholders. This action is called model deployment.

Deployment Types

To facilitate real-world business needs, there are many types of deployments:

  • Edge: To deploy the model on an edge device, where the data is produced (e.g. router).
  • Cloud: To deploy the model on a public cloud, where it can be assessed over the internet 
  • Local: To deploy the model locally on an on-premise server, which can be assessed within the intranet.

Cloud Deployment

The AI & Analytics Engine current supports cloud deployment hosted with PI.EXCHANGE. The cloud deployment can then be directly managed and monitored from within the Engine itself.

Note: This article outlines how to deploy a model.

With a cloud deployment, users can then submit prediction requests to the endpoint associated with it. Endpoints are like URLs for the deployment.