Intermediate

Integration Testing

Integration tests verify that all components of your ML system work correctly together. They catch issues that unit tests miss, such as data format mismatches, serialization bugs, and pipeline orchestration failures.

End-to-End Pipeline Testing

An end-to-end test exercises your entire ML pipeline from raw data ingestion through prediction serving. These tests use realistic (but small) datasets and verify that the complete workflow produces valid outputs within acceptable time and resource constraints.

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Best Practice: Keep a small, curated "golden dataset" that exercises all code paths in your pipeline. This dataset should include edge cases, missing values, and representative samples from each category your model handles.

API Contract Testing

Contract Aspect What to Test
Request Schema Verify that the API correctly validates input formats, required fields, data types, and value ranges.
Response Schema Confirm that responses always contain expected fields, correct data types, and valid prediction formats.
Error Handling Test that invalid inputs return appropriate error codes and messages, not stack traces or model crashes.
Performance SLAs Verify response times, throughput, and resource usage meet service-level agreements under expected load.

Testing Strategies

  1. Contract Tests

    Define contracts between services. When the model serving API changes its response format, contract tests fail immediately, alerting downstream consumers before deployment.

  2. Smoke Tests

    Quick sanity checks that verify the most critical paths work after deployment. Send a known input and verify you get a valid response within the expected latency.

  3. Load Tests

    Simulate production traffic patterns to verify your serving infrastructure handles expected load. Test with realistic batch sizes and concurrent request patterns.

  4. Chaos Tests

    Intentionally introduce failures (network latency, service crashes, corrupted inputs) to verify your system degrades gracefully and recovers automatically.

Common Integration Issues

Serialization Bugs

Model artifacts saved in one format may not load correctly in another environment. Test model save/load cycles across all target environments.

Feature Skew

Training and serving pipelines may compute features differently, causing silent prediction errors. Validate feature parity between training and inference.

Version Mismatches

Library version differences between training and serving can cause subtle behavioral changes. Pin and test exact dependency versions.

Resource Constraints

Models that run fine in development may exceed memory or CPU limits in production. Test under realistic resource constraints.

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Looking Ahead: In the next lesson, we will explore monitoring — how to track model performance in production and detect issues before they impact users.