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Deploying Models

Overview​

After building a model in Abacus.AI, the next step is to deploy it. Deploying a model makes it available for predictions and enables integration into your applications, regardless of the use case.

What You'll Learn​

In this guide, you will learn how to:

  1. Deploy a model
  2. Revert to a previous version or change the model completely

Deploying Your Model​

To deploy a model, navigate to the Model Training Page and click the Deploy Model button.

Deploy Model Button Deployment Options

Deployment Types​

When deploying, you'll need to choose between two deployment types:

  • Offline Deployment: Suitable for models that don't require real-time predictions. Ideal for ML models that run on input data using scheduled cron jobs.
  • Online Deployment: Designed for models requiring real-time predictions, such as GenAI models or ML models integrated into live applications.

Monitoring Your Deployment​

For both deployment types, you can monitor the deployment status and view logs directly from the Abacus.AI platform.

Once deployed, you can view the deployment status from the deployment page:

Deployment Status

Accessing the Prediction API​

To integrate your deployment externally, click on Prediction API to view the API documentation and calling instructions.

Prediction API

Key Deployment Settings​

Here is an overview of all important settings:

Model Deploy Generic

Auto Deployment Toggle​

  • ON: When the model connected to this deployment is retrained, the deployment automatically updates to use the new model version.
  • OFF: The deployment continues using the current model version even after retraining. You must explicitly update the deployment to point to the new model version.

Switching Model Versions​

You can select which model or version to deploy using the Switch Version Algorithm button:

Switch Version Button

Understanding Deployments and Models​

Key Concepts​

  • Deployment-Model Relationship: Each deployment is connected to a particular model version, but you can change the version or the model itself at any point in time.
  • Multiple Deployments: Each model can have multiple deployments, but each deployment is only connected to a single model version.
  • Separation: Deployments are completely separated from the model, providing flexibility in version management and deployment strategies.