The MLOps Golden Path: From Notebook to Production

The gap between a Jupyter notebook and a monitored production service is where most ML projects die. Here's the shortest path across it.

Track everything

MLflow or Weights & Biases — pick one, then log every training run: params, metrics, artifacts, data version. Non-negotiable.

Feature store as contract

Feast, Tecton, or a homegrown Delta table with strict SLAs. Training and serving must read the same features from the same code.

CI/CD for models

steps:
  - test-data-schema
  - train
  - evaluate-vs-champion
  - shadow-deploy
  - promote-if-metrics-hold

Observe, don't hope

Feature drift, prediction drift, latency, cost per inference. Alert on the leading indicators, not the outages.