CI/CD 对模型意味着什么

CI/CD means automating the steps between writing code and shipping it, so a machine checks and deploys your work instead of you doing it by hand. 🚀

Continuous Integration runs your tests automatically every time you push code, catching breakage early instead of days later when it is painful to fix.

Continuous Delivery takes code that passed CI and automatically prepares or ships it to production, so releases stop being scary manual events.

Classic CI/CD ships code. ML pipelines also ship a trained model and the data it learned from, so you now have three moving parts to track, not one.

App tests check logic. ML CI also checks model quality, asking did accuracy hold up, not just did the code run without crashing.

A code build is seconds. Training a model can take minutes or hours, so ML pipelines often train on a small sample in CI and full data only on release.

To rebuild any model exactly, you must pin its code, its data version, and its environment. CI/CD for ML makes pinning all three automatic.

In app CI/CD the output is a binary. In ML the key artifact is the trained model file, which you version, store, and later deploy.

An ML pipeline chains clear stages: lint the code, run tests, train, evaluate, and only then build the deployable image.

A trigger decides when a pipeline runs, like every push, every pull request, or a tagged release. You wire these rules once and never click again.

Without CI/CD, a model goes live only when someone remembers to run scripts. With it, every change is tested and shipped the same safe way each time.

What extra thing does ML CI/CD track that classic app CI/CD does not?

CI tests every change, CD ships it, and for ML you add the model and its data as tracked artifacts. Triggers and staged pipelines make releases safe and repeatable.