Big data ops¶
A more “grown-up” setup, where data engineers and scientists wrestle with distributed systems—often to a draw.
What can possibly go wrong?¶
The traceability tragedy¶
Models are trained, deployed, and promptly forgotten—like unfinished experiments in a mad scientist’s lab. No one knows which version is running where.
Inexplicable model drift, where yesterday’s accurate predictions become today’s gibberish.
Frantic midnight debugging sessions when the “wrong” model starts approving loans for penguins.
Yet another scandal about “rogue algorithms” making life-altering decisions with zero oversight.
The reproducibility roulette¶
A model works perfectly in staging—then fails spectacularly in production because no one thought to snapshot the dependencies.
Erratic performance, as the same input yields different results depending on which server it hits.
Endless meetings debating whether it’s a “data issue” or a “code issue” (spoiler: it’s both).
Academics cite our work as a cautionary tale in why reproducibility matters (while quietly facing the same problems).
The cost catastrophe¶
The team, drunk on the power of cloud computing, spins up a thousand GPUs to train a model that could’ve run on a toaster.
Sudden “efficiency optimisations” that strip the model down to a shadow of its former self.
The finance team starts auditing our AWS bills with the intensity of a tax inspector.
Another case study in why “big data” doesn’t always mean “smart data”.
The data-code divorce¶
Data pipelines evolve separately from model code, leading to silent failures when schemas drift.
Features mysteriously stop working because a field was renamed upstream.
The grim realisation that no one documented the schema assumptions.
A regulator somewhere drafts a new compliance rule just for us.
Hallmarks of big data ops¶
The team is a mix of data scientists (who just want to train models) and engineers (who just want to make the pipelines stop breaking).
Kafka and Spark are used religiously, even for problems that could be solved with a SQL query and a prayer.
Databricks is the communal notebook where ideas go to collide—or be abandoned.
Training happens in the cloud because no one’s laptop could handle it (and no one wants to explain why they need a £10k workstation).
Open-source tools are tolerated but distrusted—like a helpful but slightly unhinged neighbour.