How do you deploy a machine learning model and monitor it in production?

Serving patterns:

• Batch prediction — scoring on a schedule and writing results to a table. Simplest and sufficient for churn scores, lead ranking, and most business use cases.
• Real-time API — a service exposing an endpoint. Necessary when the prediction depends on the current request, such as fraud scoring at checkout.
• Embedded — the model runs on device, for latency or privacy reasons.

What must be in place:

• Reproducibility — versioned model artefacts, training data, and code, so any prediction can be traced to the model that made it.
• Train-serve consistency. The same preprocessing must run in both places. Shipping the whole pipeline as one artefact, or using a feature store, prevents a whole class of silent errors.
• A rollback path, and staged rollout — shadow mode or a canary before full traffic.

Monitoring, at three levels:

• Operational — latency, error rate, throughput.
• Data — input distributions compared against training. Data drift means the inputs have changed; catching it is possible immediately.
• Model — prediction distribution, and accuracy once labels arrive. Concept drift means the relationship between inputs and target has changed.

Note: The key practical difficulty is that ground truth often arrives late or never, so you must monitor input and prediction distributions as leading indicators rather than waiting for accuracy to confirm a problem.