Continuous integration, pipeline automation, and the machine learning tooling that is starting to sit inside them.
Notes on the machinery that moves code from a laptop into production, and on what happens when learned models start making decisions inside it. Continuous integration, workflow smells in GitHub Actions, build reliability, and the practical question underneath all of it: does this automation actually fit the project it was added to, or was it enabled because enabling it was easy? Written for engineers who maintain pipelines rather than for people who only configure them once.
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