Model Serving and API integration

Databricks Model Serving provides managed interfaces for supported model workloads, including real-time inference patterns. Separate the endpoint boundary from application logic so authentication, request validation, retries, and response handling remain explicit. Confirm the endpoint type, model compatibility, region, permissions, and limits in the current workspace documentation; availability and configuration can differ. Never embed endpoint credentials in browser code or source control. Use an appropriate service identity, store secrets in an approved secret-management mechanism, and rotate credentials. Set timeouts and bounded retries, and return clear errors when a dependency is unavailable. Treat a serving endpoint like any other production dependency. Define a versioned request and response schema, authenticate with an application identity, validate payload size and types, and keep credentials on the server side. Set connection and response timeouts, bounded retries, and a clear error contract. Exercise permission failures, rate limits, unavailable endpoints, and malformed model output. Before relying on a Databricks feature, verify model compatibility, workspace configuration, region, and endpoint limits in current documentation; these details may vary across deployments. Implementation drill: trace one representative request through the stages in the diagram. Write down what enters each stage, what output or evidence it produces, and which identity is allowed to perform it. Then test an empty input, an unauthorized request, a dependency timeout, and an unexpected result. For each case, state whether the application should retry, reject, abstain, or ask a person for help. Keep the observed failure as a regression example so changes to prompts, models, data, or code can be checked against the same behavior. Practice task: Where should production endpoint credentials be stored?