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🚦 CI/CD for Machine Learning Pipelines

How continuous integration and continuous delivery practices adapt to machine learning: testing data, models and deployments automatically.

AI & Machine Learning3DAdvanced60 FPS

Watch model builds travel through an automated ML pipeline — data validation, training, an accuracy gate, a model registry and a canary deployment — with auto-rollback protecting production from bad releases.

🔬 What It Demonstrates

Unlike ordinary software CI/CD, ML pipelines must gate on data quality and model quality, not just code tests. Builds that fail the data-validation or accuracy gate never reach the registry; canary releases catch live incidents before a full rollout.

🎮 How to Use

Adjust commit frequency, data drift risk and the minimum accuracy gate to see how many builds get blocked. Tune canary traffic and toggle auto-rollback to see how incidents are caught — or silently ship — in production.

💡 Did You Know?

In real MLOps systems, silent data drift is one of the most common causes of production model failure — the code and tests can all pass while the underlying data distribution quietly shifts.

⚙ Under the hood

How continuous integration and continuous delivery practices adapt to machine learning: testing data, models and deployments automatically.

machine learningci-cdpipelinesautomationtestingdeploymentThree.js

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install