A comprehensive guide to testing ML systems in production using pytest, covering unit tests for inference services, integration tests with FastAPI TestClient, and load testing with Locust. Includes pytest fixtures via conftest.py, test directory structure (unit/integration/performance), YAML-based test configuration, environment variable isolation, and code quality tools (flake8, Black, isort, MyPy). Also explains key load testing metrics like RPS, P95/P99 latency, and how to run Locust in headless mode for CI/CD pipelines.
Table of contents
Pytest Tutorial: MLOps Testing, Fixtures, and Locust Load TestingIntroduction to MLOps Testing: Building Reliable ML Systems with PytestWhy Testing Is Non-Negotiable in MLOpsTest-Driven MLOps: Applying Software Testing Best Practices to ML PipelinesProject Structure and Test LayoutUnit Testing in MLOps with PytestIntegration Testing in MLOpsPerformance and Load Testing with LocustMLOps Test Configuration: YAML and Environment VariablesCode Quality in MLOps: Linting, Formatting, and Static Analysis ToolsAutomating Testing with a Pytest Test Runner ScriptAutomating Load Testing in MLOps with Locust ScriptsTest Coverage in MLOps: Measuring and Improving Code CoverageSummarySort: