Reproducibility is one of those things that separates hobby projects from production-ready ML work, and Docker is often the unsung hero here. This guide covers practical tricks for treating containers as proper artifacts rather than throwaway environments. Worth bookmarking if you've ever had a model work perfectly on your machine and nowhere else
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