ML development: Where dreams are made (and immediately compromised)¶

The genesis of an MLOps “journey” (read: ordeal) begins here, in the warm glow of a freshly opened IDE. You’re not just coding—you’re architecting the future. Or so you tell yourself.

The problem-solution mirage¶

Before you drown in Dockerfiles, you must first define the problem. This is where stakeholders say things like “Can’t AI just do it?” and data scientists nod politely while silently calculating how to reframe “impossible” as “iterative MVP.” Clarity is key—or so claims every Medium article ever. In practice, “clear requirements” often mutate hourly, like a virus exposed to too many Slack threads.

Tooling: The illusion of control¶

Next, you’ll choose “the right tools.” Will it be PyTorch or TensorFlow? S3 or Snowflake? Kubernetes or… actually, no one chooses Kubernetes willingly; it’s thrust upon you by a DevOps engineer with a thousand-yard stare. This is where you pretend infrastructure decisions are rational, rather than the result of a coin toss or which vendor bought the team lunch last.

The development delusion¶

With your stack “finalised” (lol), you’ll build “reproducible” environments. Spoiler: They’ll work once—on your machine—before collapsing under the weight of conflicting dependencies. Training begins, fueled by optimism and free-tier cloud credits. You’ll tweak hyperparameters like a medieval alchemist, chasing that elusive F1 score, only to realise your “test data” was just the training set with a different filename.

Packaging: The first betrayal¶

At last, a model exists. Now you must serialise it, containerise it, and shove it into the CI/CD pipeline. This is where Python’s “It works on my laptop!” meets the cold reality of production’s “What’s a CUDA driver?” You’ll spend hours wrestling with Conda environments, only to discover that “ML model as a microservice” is just a euphemism for “I hope you like debugging HTTP 500 errors.”