Understand deployments

In H2O MLOps, deployments are created when model version(s) are served for scoring. Model endpoint security, artifact type, runtime, and Kubernetes options can be configured when deploying a model.

H2O MLOps supports different deployment modes:

• Real-time deployments: Make a model available as a live REST endpoint that returns predictions immediately when given input data. Types of real-time deployments include:

  • Single model deployments: Serve one model version at a time.
  • A/B test deployments: Compare the performance of two or more models in production.
  • Champion/Challenger deployments: Continuously compare a Champion model against one or more Challenger models to promote the best performer.

• Batch scoring deployments: Run model scoring jobs on batches of data instead of serving predictions in real time.

You can create and manage deployments using:

• The H2O MLOps UI on H2O AI Cloud.
• The H2O MLOps Python client, which allows you to automate deployment tasks from a Python application.

To learn more about deployments, refer to the following pages:

• Create a deployment
• View deployments
• Scoring runtimes
• Vertical Pod Autoscaler (VPA) support
• Pod Disruption Budget (PDB)

To learn more about deployments using the Python client, see:

• Configure deployments
• Manage deployments
• Deployment scorer

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