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Integration test

As explained in Machine Learning System Design

A test that evaluates interactions among multiple software components or between a component and an external system.

An integration test checks whether multiple components work harmoniously together, especially when releasing infrastructure such as a model wrapper that depends on software, services, or system requirements. section 13.5.1

Use integration testing to:

  • Verify that wrapper changes do not disrupt existing services. section 13.5.1
  • Check interactions with software updates, third-party services, and system requirements. section 13.5.1
  • Validate that preprocessing, feature engineering, and related pipelines remain consistent during model rollout. section 13.5.1

Supermegaretail wrapper release

Before releasing a demand-prediction wrapper, run comprehensive integration tests across its services and dependencies. Confirm backward compatibility so existing services continue operating, then monitor system health, uptime, response times, and error rates after deployment. section 13.5.1

GET /predictions?query=<query_string>&parameters=<parameters>&version=
<version>
&limit=<limit>&request_id=<request_id>&sku=<sku>&entity_id=<entity_id>
&group=<group_type>

Use the prediction endpoint as the integration boundary, checking that requests carrying query, parameters, version, and entity identifiers reach the correct services. section 13.5.1

How Machine Learning System Design covers it

How deep it goes
Explains comprehensive testing and backward compatibility but does not detail individual test cases or implementation techniques.
Examples
Uses a demand-prediction wrapper, third-party services, existing services, and a prediction API as integration contexts.
What you can do after
Diagnose release risks by checking component compatibility, pipeline consistency, and post-deployment system health.
How it connects
Connects wrapper releases with model releases, backward compatibility, offline validation, shadow mode, preprocessing, feature engineering, and monitoring.

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This explainer was generated by Manning's knowledge graph from the text of Machine Learning System Design. The section links point to the parts of the book it draws on.