From Jupyter to Production ML Pipelines
Moving a notebook experiment into a repeatable training + inference path without losing your sanity.
Aug 12, 2025•12 min read
The notebook trap
Notebooks are great for exploration. They are fragile as the source of truth for training.
Pin everything
Versions for Python, CUDA, and packages. Reproducibility beats clever one-off cells.
Pipeline shape
Extract data loading, training, and evaluation into plain modules. CI can run tests on those modules.
Inference
Separate training artifacts from serving. Containerize when you cross team boundaries.
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