The beginning of hope and notebooks¶

A model is born in a notebook one wet Tuesday, full of promise and two hundred lines of borrowed code. The Jupyter scroll glows around it like an enchanted parchment, and its maker feels briefly like a wizard who has discovered a new spell. Nobody tells the model that most of its kind do not survive first contact with production, and that the ones that do often wish they had not.

Its conception is chaotic. Stakeholders ask whether the AI cannot just do it, the engineer nods and quietly recasts “impossible” as “iterative MVP”, and the requirements change shape between one message and the next. The model, still damp from initialisation, has no idea what it is meant to become, which puts it on roughly equal footing with everyone else in the room.

Then the tooling gods are consulted. PyTorch or TensorFlow, S3 or Snowflake, Kubernetes or something that induces nightmares. The choices are rarely rational; more often a vendor bought lunch and the whole stack tilted in gratitude. The model is evaluated on data that is its training data in fancy dress, its hyperparameters twiddled until the numbers improve, its environment reproducible right up until the cloud discovers it does not care for local whims. At last the model is serialised, containerised, and posted into the pipeline like a parcel with no return address.

This is the moment it leaves home. It does not know yet that home was the safest it will ever be.