Study Databricks ML-PRO Model Deployment: key concepts, common traps, and exam decision cues.
This domain is smaller by weight, but it punishes fragile rollout thinking. Databricks wants deployment paths that reduce blast radius and preserve operational control.
| Lesson | Focus |
|---|---|
| 3.1 Blue-Green, Canary and Rollout Safety with Model Serving | Learn how ML-PRO frames rollout safety for live traffic. |
| 3.2 Custom PyFunc Models, Serving Endpoints and Deployment Interfaces | Learn how Databricks expects you to deploy and query custom model objects. |
| If the question is really about… | Go first to… |
|---|---|
| blue-green, canary, rollout safety, or high-traffic serving strategy | 3.1 Blue-Green, Canary and Rollout Safety with Model Serving |
| custom PyFunc models, custom artifacts, REST API, or MLflow Deployments SDK | 3.2 Custom PyFunc Models, Serving Endpoints and Deployment Interfaces |