Title: Discussion 14 — Why Doesn't Everyone Test Their Pipelines?
Course: AIINFRA 101: Applied Infrastructure & Containerization > Section: Week 14 — CI/CD for ML with GitHub Actions

💬 Discussion 14 — Why Doesn't Everyone Test Their Pipelines? (20 pts)

CI/CD and data-quality gates are free (for public repos), automatic once set up, and catch real failures before they reach production. Yet plenty of real-world teams skip CI/CD entirely, set it up once and let it rot, or rigorously test their code while barely touching their data. Post a response addressing:

- Is the technology the barrier here, or is it something else — the learning curve of YAML, unclear ownership of "who fixes it when it breaks," or the fact that data testing (unlike code testing) has no equivalent of pytest as a cultural default?
- Think back to this week's misconceptions: the silent .github/workflow/ typo, secrets that leak past masking, evaluating a model only on training data, and tagging images :latest. Do small mistakes like these discourage teams from adopting rigorous CI, or are they a one-time learning cost?
- Pick one gate from this week (a workflow trigger, a data expectation, or a model-quality threshold) and describe a real-world mistake it would have caught.

Respond to at least two classmates, engaging with a specific point they made rather than restating your own view.