I deployed a model manually once. Configuration error brought down the service for 4 hours.

Never again.

ML CI/CD is different from software CI/CD in ways that trip people up. With software, you test the code. With ML, you test the code AND the data AND the model AND their interactions.

A proper ML CI/CD pipeline: code changes trigger unit tests. Data validation checks run automatically. If data changes, model training kicks off. The new model is evaluated against the current baseline (not just tested in isolation). If it's better, it gets A/B deployed. Monitoring alerts are configured automatically. If metrics drop below threshold, automatic rollback.

I've automated this with GitHub Actions on multiple projects. The initial setup takes a few days. But it's paid for itself hundreds of times over by preventing exactly the kind of manual deployment mistake that cost me those 4 hours.

In 2026, manually deploying ML models is genuinely engineering malpractice. Not because automation is fancy, but because the risks of manual deployment are too high when models affect real decisions for real people.

Set it up once. Sleep better forever.