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Interactive 3D/CI/CD Pipeline for Machine Learning
CI/CD Pipeline for ML
Code commit triggers the full pipeline. Inject a failure at any stage to see how it blocks downstream steps.
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Code Commit
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Unit Tests
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Data Validation
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Model Training
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Model Evaluation
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Integration Tests
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Deploy → Staging
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Deploy → Production
PENDING
Code Commit
git push trigger
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PENDING
Unit Tests
pytest, 243 tests
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PENDING
Data Validation
Great Expectations
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PENDING
Model Training
GPU job, ~12 min
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PENDING
Model Evaluation
Quality gates
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PENDING
Integration Tests
end-to-end API
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PENDING
Deploy → Staging
k8s canary 5%
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PENDING
Deploy → Production
k8s full rollout
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Run Pipeline
Inject Failure At
Display
Key Insight
Fail fast. A failure in unit tests saves 12 minutes of GPU training time. Every gate that fails early saves downstream compute cost.

CI/CD Pipeline for Machine Learning - Interactive Visualization

A CI/CD pipeline for ML automates the entire journey from code commit to production model. Every push triggers unit tests, data validation, model training, evaluation against quality gates, integration tests, and finally deployment. Failures at any stage halt the pipeline, preventing broken models from reaching production. The fail-fast principle saves GPU compute: a failing unit test at 10 seconds prevents wasted hours of training.

  • Run the full pipeline and watch stages animate from pending through running to passed
  • Inject a failure at any stage to see how downstream stages are blocked and skipped
  • Stage durations are realistic - unit tests in seconds, GPU training in minutes
  • Reset and re-run to compare how different failure points affect total pipeline time

Part of the EngineersOfAI Interactive 3D - free interactive visualizations covering every major concept in machine learning and AI engineering. Hover any element for a plain-English explanation. No code required.