A model that works in a notebook is maybe 10% of the work. The other 90% is data pipelines, serving infrastructure, monitoring, and retraining loops. This guide walks through what it actually takes to run ML in production.
Start with the Data Contract
Most production ML failures are data failures. Before training anything, define a schema for your inputs and validate it at every boundary — ingestion, training, and inference must agree on what a valid record looks like.
Reproducible Training
Pin everything: data snapshots, feature code, hyperparameters, and random seeds. If you can't recreate last month's model bit-for-bit, you can't debug why this month's is worse.
train_job = pipeline.train(
dataset="events@2024-12-01",
features="v14",
seed=42,
)Serving at Scale
Batch predictions cover more use cases than teams expect, and they're an order of magnitude cheaper to operate than real-time endpoints. Reach for online inference only when the product genuinely needs sub-second decisions.
"The best ML infrastructure is boring. Excitement in your serving layer is called an incident."
Monitor the Model, Not Just the Service
Uptime dashboards won't tell you the model quietly degraded. Track prediction distributions, feature drift, and — where labels arrive later — realized accuracy over time. Alert on shifts, retrain on schedule, and keep a rollback path to the previous model version.
